pacman::p_load(tidyverse, janitor, ppcor, foreign, renv, ltm, pastecs)
data <- read.spss("AutonomicFinal042523.sav", use.value.labels=TRUE, max.value.labels=Inf, to.data.frame=TRUE)Analyses
Recodes and Scales
Dummy Codes
These numbers contain the missing values, to get sample descriptive (e.g., race/ethnicity) the numbers must be calculated with the data frames in the Wrangling section. in other words, run all chunks then run tabyl(FSFSurveyT1$race_eth) in the console.
# Sex
data$Genderfactor <-as.factor(data$Gender)
data$GenderNumb <-as.numeric(data$Genderfactor)
table(data$GenderNumb)
1 2
80 26 data$Female <- data$GenderNumb
data$Female = ifelse(data$GenderNumb == 1, 1, data$Female)
data$Female = ifelse(data$GenderNumb == 2, 0, data$Female)
table(data$Female)
0 1
26 80 table(data$GenderNumb)
1 2
80 26 data$Male <- data$GenderNumb
data$Male = ifelse(data$GenderNumb == 1, 0, data$Male)
data$Male = ifelse(data$GenderNumb == 2, 1, data$Male)
table(data$Male)
0 1
80 26 # Race
# Dem percents
table(data$race_eth)
American Indian/Native American Asian or Pacific Islander
1 6
Black/African American Hispanic
10 26
Multiracial Other
4 1
White
58 data$race_eth1 <- as.factor(data$race_eth)
data$race_eth2 <- as.numeric(data$race_eth1)
table(data$race_eth2)
1 2 3 4 5 6 7
1 6 10 26 4 1 58 data$race_ethDC <- data$race_eth2
data$race_ethDC = ifelse(data$race_eth2 == 7, 8, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 6, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 5, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 4, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 3, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 2, 0, data$race_ethDC)
data$race_ethDC = ifelse(data$race_eth2 == 1, 0, data$race_ethDC)
table(data$race_ethDC)
0 8
48 58 data$White = data$race_ethDC
data$White <- as.factor(data$White)
data$White = ifelse(data$race_ethDC == 8, 1, data$White)
data$White = ifelse(data$race_ethDC == 0, 0, data$White)
table(data$White)
0 1
48 58 SRP Reverse Codes
IPM (16, 24, 31, 38, 61) CA (11, 19, 23, 26, 44) ELS (14, 22, 25, 36, 47) ASB (5, 6, 18, 21, 34, 46)
Citation: Paulhus, D.L., Neumann, C. S., & Hare, R.D. (in press). Manual for the Self-Report Psychopathy scale 4th edition. Toronto: Multi-Health Systems.
# IPM
table(data$SRP_16n)
1 2 3 4 5
2 18 36 33 17 data$SRP16nRev = data$SRP_16n
data$SRP16nRev = ifelse(data$SRP_16n == 1, 5, data$SRP16nRev)
data$SRP16nRev = ifelse(data$SRP_16n == 2, 4, data$SRP16nRev)
data$SRP16nRev = ifelse(data$SRP_16n == 4, 2, data$SRP16nRev)
data$SRP16nRev = ifelse(data$SRP_16n == 5, 1, data$SRP16nRev)
table(data$SRP16nRev)
1 2 3 4 5
17 33 36 18 2 table(data$SRP_24n)
1 2 3 4 5
5 19 24 46 12 data$SRP24nRev = data$SRP_24n
data$SRP24nRev = ifelse(data$SRP_24n == 1, 5, data$SRP24nRev)
data$SRP24nRev = ifelse(data$SRP_24n == 2, 4, data$SRP24nRev)
data$SRP24nRev = ifelse(data$SRP_24n == 4, 2, data$SRP24nRev)
data$SRP24nRev = ifelse(data$SRP_24n == 5, 1, data$SRP24nRev)
table(data$SRP24nRev)
1 2 3 4 5
12 46 24 19 5 table(data$SRP_31n)
1 2 3 4 5
6 15 46 30 9 data$SRP31nRev = data$SRP_31n
data$SRP31nRev = ifelse(data$SRP_31n == 1, 5, data$SRP31nRev)
data$SRP31nRev = ifelse(data$SRP_31n == 2, 4, data$SRP31nRev)
data$SRP31nRev = ifelse(data$SRP_31n == 4, 2, data$SRP31nRev)
data$SRP31nRev = ifelse(data$SRP_31n == 5, 1, data$SRP31nRev)
table(data$SRP31nRev)
1 2 3 4 5
9 30 46 15 6 table(data$SRP_38n)
1 2 3 4 5
4 18 28 36 20 data$SRP38nRev = data$SRP_38n
data$SRP38nRev = ifelse(data$SRP_38n == 1, 5, data$SRP38nRev)
data$SRP38nRev = ifelse(data$SRP_38n == 2, 4, data$SRP38nRev)
data$SRP38nRev = ifelse(data$SRP_38n == 4, 2, data$SRP38nRev)
data$SRP38nRev = ifelse(data$SRP_38n == 5, 1, data$SRP38nRev)
table(data$SRP38nRev)
1 2 3 4 5
20 36 28 18 4 table(data$SRP_61n)
1 2 3 4 5
5 14 15 37 35 data$SRP61nRev = data$SRP_61n
data$SRP61nRev = ifelse(data$SRP_61n == 1, 5, data$SRP61nRev)
data$SRP61nRev = ifelse(data$SRP_61n == 2, 4, data$SRP61nRev)
data$SRP61nRev = ifelse(data$SRP_61n == 4, 2, data$SRP61nRev)
data$SRP61nRev = ifelse(data$SRP_61n == 5, 1, data$SRP61nRev)
table(data$SRP61nRev)
1 2 3 4 5
35 37 15 14 5 # CA
table(data$SRP_11n)
1 2 3 4 5
2 7 10 43 44 data$SRP11nRev = data$SRP_11n
data$SRP11nRev = ifelse(data$SRP_11n == 1, 5, data$SRP11nRev)
data$SRP11nRev = ifelse(data$SRP_11n == 2, 4, data$SRP11nRev)
data$SRP11nRev = ifelse(data$SRP_11n == 4, 2, data$SRP11nRev)
data$SRP11nRev = ifelse(data$SRP_11n == 5, 1, data$SRP11nRev)
table(data$SRP11nRev)
1 2 3 4 5
44 43 10 7 2 table(data$SRP_19n)
2 3 4 5
8 14 54 29 data$SRP19nRev = data$SRP_19n
data$SRP19nRev = ifelse(data$SRP_19n == 1, 5, data$SRP19nRev)
data$SRP19nRev = ifelse(data$SRP_19n == 2, 4, data$SRP19nRev)
data$SRP19nRev = ifelse(data$SRP_19n == 4, 2, data$SRP19nRev)
data$SRP19nRev = ifelse(data$SRP_19n == 5, 1, data$SRP19nRev)
table(data$SRP19nRev)
1 2 3 4
29 54 14 8 table(data$SRP_23n)
1 2 3 4 5
28 37 15 14 12 data$SRP23nRev = data$SRP_23n
data$SRP23nRev = ifelse(data$SRP_23n == 1, 5, data$SRP23nRev)
data$SRP23nRev = ifelse(data$SRP_23n == 2, 4, data$SRP23nRev)
data$SRP23nRev = ifelse(data$SRP_23n == 4, 2, data$SRP23nRev)
data$SRP23nRev = ifelse(data$SRP_23n == 5, 1, data$SRP23nRev)
table(data$SRP23nRev)
1 2 3 4 5
12 14 15 37 28 table(data$SRP_26n)
2 3 4 5
6 25 49 26 data$SRP26nRev = data$SRP_26n
data$SRP26nRev = ifelse(data$SRP_26n == 1, 5, data$SRP26nRev)
data$SRP26nRev = ifelse(data$SRP_26n == 2, 4, data$SRP26nRev)
data$SRP26nRev = ifelse(data$SRP_26n == 4, 2, data$SRP26nRev)
data$SRP26nRev = ifelse(data$SRP_26n == 5, 1, data$SRP26nRev)
table(data$SRP26nRev)
1 2 3 4
26 49 25 6 table(data$SRP_44n)
1 2 3 4 5
1 6 20 53 26 data$SRP44nRev = data$SRP_44n
data$SRP44nRev = ifelse(data$SRP_44n == 1, 5, data$SRP44nRev)
data$SRP44nRev = ifelse(data$SRP_44n == 2, 4, data$SRP44nRev)
data$SRP44nRev = ifelse(data$SRP_44n == 4, 2, data$SRP44nRev)
data$SRP44nRev = ifelse(data$SRP_44n == 5, 1, data$SRP44nRev)
table(data$SRP44nRev)
1 2 3 4 5
26 53 20 6 1 # ELS
table(data$SRP_14n)
1 2 3 4 5
8 13 17 40 27 data$SRP14nRev = data$SRP_14n
data$SRP14nRev = ifelse(data$SRP_14n == 1, 5, data$SRP14nRev)
data$SRP14nRev = ifelse(data$SRP_14n == 2, 4, data$SRP14nRev)
data$SRP14nRev = ifelse(data$SRP_14n == 4, 2, data$SRP14nRev)
data$SRP14nRev = ifelse(data$SRP_14n == 5, 1, data$SRP14nRev)
table(data$SRP14nRev)
1 2 3 4 5
27 40 17 13 8 table(data$SRP_22n)
1 2 3 4 5
5 29 15 35 22 data$SRP22nRev = data$SRP_22n
data$SRP22nRev = ifelse(data$SRP_22n == 1, 5, data$SRP22nRev)
data$SRP22nRev = ifelse(data$SRP_22n == 2, 4, data$SRP22nRev)
data$SRP22nRev = ifelse(data$SRP_22n == 4, 2, data$SRP22nRev)
data$SRP22nRev = ifelse(data$SRP_22n == 5, 1, data$SRP22nRev)
table(data$SRP22nRev)
1 2 3 4 5
22 35 15 29 5 table(data$SRP_25n)
1 2 3 4 5
9 35 37 16 9 data$SRP25nRev = data$SRP_25n
data$SRP25nRev = ifelse(data$SRP_25n == 1, 5, data$SRP25nRev)
data$SRP25nRev = ifelse(data$SRP_25n == 2, 4, data$SRP25nRev)
data$SRP25nRev = ifelse(data$SRP_25n == 4, 2, data$SRP25nRev)
data$SRP25nRev = ifelse(data$SRP_25n == 5, 1, data$SRP25nRev)
table(data$SRP25nRev)
1 2 3 4 5
9 16 37 35 9 table(data$SRP_36n)
1 2 3 4 5
8 21 15 25 37 data$SRP36nRev = data$SRP_36n
data$SRP36nRev = ifelse(data$SRP_36n == 1, 5, data$SRP36nRev)
data$SRP36nRev = ifelse(data$SRP_36n == 2, 4, data$SRP36nRev)
data$SRP36nRev = ifelse(data$SRP_36n == 4, 2, data$SRP36nRev)
data$SRP36nRev = ifelse(data$SRP_36n == 5, 1, data$SRP36nRev)
table(data$SRP36nRev)
1 2 3 4 5
37 25 15 21 8 table(data$SRP_47n)
1 2 3 4 5
8 54 26 13 5 data$SRP47nRev = data$SRP_47n
data$SRP47nRev = ifelse(data$SRP_47n == 1, 5, data$SRP47nRev)
data$SRP47nRev = ifelse(data$SRP_47n == 2, 4, data$SRP47nRev)
data$SRP47nRev = ifelse(data$SRP_47n == 4, 2, data$SRP47nRev)
data$SRP47nRev = ifelse(data$SRP_47n == 5, 1, data$SRP47nRev)
table(data$SRP47nRev)
1 2 3 4 5
5 13 26 54 8 # ASB
table(data$SRP_05n)
1 2 3 4 5
15 8 2 16 65 data$SRP5nRev = data$SRP_05n
data$SRP5nRev = ifelse(data$SRP_05n == 1, 5, data$SRP5nRev)
data$SRP5nRev = ifelse(data$SRP_05n == 2, 4, data$SRP5nRev)
data$SRP5nRev = ifelse(data$SRP_05n == 4, 2, data$SRP5nRev)
data$SRP5nRev = ifelse(data$SRP_05n == 5, 1, data$SRP5nRev)
table(data$SRP5nRev)
1 2 3 4 5
65 16 2 8 15 table(data$SRP_06n)
1 2 4 5
10 4 17 75 data$SRP6nRev = data$SRP_06n
data$SRP6nRev = ifelse(data$SRP_06n == 1, 5, data$SRP6nRev)
data$SRP6nRev = ifelse(data$SRP_06n == 2, 4, data$SRP6nRev)
data$SRP6nRev = ifelse(data$SRP_06n == 4, 2, data$SRP6nRev)
data$SRP6nRev = ifelse(data$SRP_06n == 5, 1, data$SRP6nRev)
table(data$SRP6nRev)
1 2 4 5
75 17 4 10 table(data$SRP_18n)
1 2 4 5
6 1 12 87 data$SRP18nRev = data$SRP_18n
data$SRP18nRev = ifelse(data$SRP_18n == 1, 5, data$SRP18nRev)
data$SRP18nRev = ifelse(data$SRP_18n == 2, 4, data$SRP18nRev)
data$SRP18nRev = ifelse(data$SRP_18n == 4, 2, data$SRP18nRev)
data$SRP18nRev = ifelse(data$SRP_18n == 5, 1, data$SRP18nRev)
table(data$SRP18nRev)
1 2 4 5
87 12 1 6 table(data$SRP_21n)
1 2 3 4 5
6 13 8 24 55 data$SRP21nRev = data$SRP_21n
data$SRP21nRev = ifelse(data$SRP_21n == 1, 5, data$SRP21nRev)
data$SRP21nRev = ifelse(data$SRP_21n == 2, 4, data$SRP21nRev)
data$SRP21nRev = ifelse(data$SRP_21n == 4, 2, data$SRP21nRev)
data$SRP21nRev = ifelse(data$SRP_21n == 5, 1, data$SRP21nRev)
table(data$SRP21nRev)
1 2 3 4 5
55 24 8 13 6 table(data$SRP_34n)
1 2 3 4 5
3 4 1 13 85 data$SRP34nRev = data$SRP_34n
data$SRP34nRev = ifelse(data$SRP_34n == 1, 5, data$SRP34nRev)
data$SRP34nRev = ifelse(data$SRP_34n == 2, 4, data$SRP34nRev)
data$SRP34nRev = ifelse(data$SRP_34n == 4, 2, data$SRP34nRev)
data$SRP34nRev = ifelse(data$SRP_34n == 5, 1, data$SRP34nRev)
table(data$SRP34nRev)
1 2 3 4 5
85 13 1 4 3 table(data$SRP_46n)
1 2 3 4 5
18 21 2 16 49 data$SRP46nRev = data$SRP_46n
data$SRP46nRev = ifelse(data$SRP_46n == 1, 5, data$SRP46nRev)
data$SRP46nRev = ifelse(data$SRP_46n == 2, 4, data$SRP46nRev)
data$SRP46nRev = ifelse(data$SRP_46n == 4, 2, data$SRP46nRev)
data$SRP46nRev = ifelse(data$SRP_46n == 5, 1, data$SRP46nRev)
table(data$SRP46nRev)
1 2 3 4 5
49 16 2 21 18 Levenson Reverse Codes
(3, 7, 10, 13, 15, 21, 26)
Bold missing from half of surveys
See code book to match the questions in figure to the numeric values in survey.
Citation: Levenson, M. R., Kiehl, K. A., & Fitzpatrick, C. M. (1995). Assessing psychopathic attributes in a noninstitutionalized population. Journal of Personality and Social Psychology, 68(1), 151–158.
table(data$Lev_10n)
1 2 3 4
5 23 52 26 data$Lev_10nRev = data$Lev_10n
data$Lev_10nRev = ifelse(data$Lev_10n == 1, 4, data$Lev_10nRev)
data$Lev_10nRev = ifelse(data$Lev_10n == 2, 3, data$Lev_10nRev)
data$Lev_10nRev = ifelse(data$Lev_10n == 3, 2, data$Lev_10nRev)
data$Lev_10nRev = ifelse(data$Lev_10n == 4, 1, data$Lev_10nRev)
table(data$Lev_10nRev)
1 2 3 4
26 52 23 5 table(data$Lev_13n)
1 2 3 4
3 9 41 53 data$Lev_13nRev = data$Lev_12n
data$Lev_13nRev = ifelse(data$Lev_13n == 1, 4, data$Lev_13nRev)
data$Lev_13nRev = ifelse(data$Lev_13n == 2, 3, data$Lev_13nRev)
data$Lev_13nRev = ifelse(data$Lev_13n == 3, 2, data$Lev_13nRev)
data$Lev_13nRev = ifelse(data$Lev_13n == 4, 1, data$Lev_13nRev)
table(data$Lev_13nRev)
1 2 3 4
53 41 9 3 table(data$Lev_15n)
1 2 3 4
3 8 32 17 data$Lev_15nRev = data$Lev_15n
data$Lev_15nRev = ifelse(data$Lev_15n == 1, 4, data$Lev_15nRev)
data$Lev_15nRev = ifelse(data$Lev_15n == 2, 3, data$Lev_15nRev)
data$Lev_15nRev = ifelse(data$Lev_15n == 3, 2, data$Lev_15nRev)
data$Lev_15nRev = ifelse(data$Lev_15n == 4, 1, data$Lev_15nRev)
table(data$Lev_15nRev)
1 2 3 4
17 32 8 3 table(data$Lev_21n)
1 2 3 4
1 6 46 53 data$Lev_21nRev = data$Lev_21n
data$Lev_21nRev = ifelse(data$Lev_21n == 1, 4, data$Lev_21nRev)
data$Lev_21nRev = ifelse(data$Lev_21n == 2, 3, data$Lev_21nRev)
data$Lev_21nRev = ifelse(data$Lev_21n == 3, 2, data$Lev_21nRev)
data$Lev_21nRev = ifelse(data$Lev_21n == 4, 1, data$Lev_21nRev)
table(data$Lev_21nRev)
1 2 3 4
53 46 6 1 table(data$Lev_26n)
1 2 3 4
6 3 54 43 data$Lev_26nRev = data$Lev_26n
data$Lev_26nRev = ifelse(data$Lev_26n == 1, 4, data$Lev_26nRev)
data$Lev_26nRev = ifelse(data$Lev_26n == 2, 3, data$Lev_26nRev)
data$Lev_26nRev = ifelse(data$Lev_26n == 3, 2, data$Lev_26nRev)
data$Lev_26nRev = ifelse(data$Lev_26n == 4, 1, data$Lev_26nRev)
table(data$Lev_26nRev)
1 2 3 4
43 54 3 6 table(data$Lev_03n)
1 2 3 4
1 12 56 37 data$Lev_03nRev = data$Lev_03n
data$Lev_03nRev = ifelse(data$Lev_03n == 1, 4, data$Lev_03nRev)
data$Lev_03nRev = ifelse(data$Lev_03n == 2, 3, data$Lev_03nRev)
data$Lev_03nRev = ifelse(data$Lev_03n == 3, 2, data$Lev_03nRev)
data$Lev_03nRev = ifelse(data$Lev_03n == 4, 1, data$Lev_03nRev)
table(data$Lev_03nRev)
1 2 3 4
37 56 12 1 table(data$Lev_07n)
1 2 3 4
1 24 56 24 data$Lev_07nRev = data$Lev_07n
data$Lev_07nRev = ifelse(data$Lev_07n == 1, 4, data$Lev_07nRev)
data$Lev_07nRev = ifelse(data$Lev_07n == 2, 3, data$Lev_07nRev)
data$Lev_07nRev = ifelse(data$Lev_07n == 3, 2, data$Lev_07nRev)
data$Lev_07nRev = ifelse(data$Lev_07n == 4, 1, data$Lev_07nRev)
table(data$Lev_07nRev)
1 2 3 4
24 56 24 1 ICU
(1, 3, 5, 8, 13, 14, 15, 16, 17, 19, 23, 24)
Essau, C. A., Sasagawa, S., & Frick, P. J. (2006). Callous-unemotional traits in a community sample of adolescents. Assessment, 13(4), 454-469.
# Callous
table(data$ICU_8n)
2 3 4
16 56 34 data$ICU_8nRev = data$ICU_8n
data$ICU_8nRev = ifelse(data$ICU_8n == 1, 4, data$ICU_8nRev)
data$ICU_8nRev = ifelse(data$ICU_8n == 2, 3, data$ICU_8nRev)
data$ICU_8nRev = ifelse(data$ICU_8n == 3, 2, data$ICU_8nRev)
data$ICU_8nRev = ifelse(data$ICU_8n == 4, 1, data$ICU_8nRev)
table(data$ICU_8nRev)
1 2 3
34 56 16 # Uncaring
table(data$ICU_15n)
1 2 3 4
1 13 41 51 data$ICU_15nRev = data$ICU_15n
data$ICU_15nRev = ifelse(data$ICU_15n == 1, 4, data$ICU_15nRev)
data$ICU_15nRev = ifelse(data$ICU_15n == 2, 3, data$ICU_15nRev)
data$ICU_15nRev = ifelse(data$ICU_15n == 3, 2, data$ICU_15nRev)
data$ICU_15nRev = ifelse(data$ICU_15n == 4, 1, data$ICU_15nRev)
table(data$ICU_15nRev)
1 2 3 4
51 41 13 1 table(data$ICU_23n)
1 2 3 4
1 16 40 49 data$ICU_23nRev = data$ICU_23n
data$ICU_23nRev = ifelse(data$ICU_23n == 1, 4, data$ICU_23nRev)
data$ICU_23nRev = ifelse(data$ICU_23n == 2, 3, data$ICU_23nRev)
data$ICU_23nRev = ifelse(data$ICU_23n == 3, 2, data$ICU_23nRev)
data$ICU_23nRev = ifelse(data$ICU_23n == 4, 1, data$ICU_23nRev)
table(data$ICU_23nRev)
1 2 3 4
49 40 16 1 table(data$ICU_16n)
2 3 4
11 43 52 data$ICU_16nRev = data$ICU_16n
data$ICU_16nRev = ifelse(data$ICU_16n == 1, 4, data$ICU_16nRev)
data$ICU_16nRev = ifelse(data$ICU_16n == 2, 3, data$ICU_16nRev)
data$ICU_16nRev = ifelse(data$ICU_16n == 3, 2, data$ICU_16nRev)
data$ICU_16nRev = ifelse(data$ICU_16n == 4, 1, data$ICU_16nRev)
table(data$ICU_16nRev)
1 2 3
52 43 11 table(data$ICU_3n)
1 2 3 4
1 3 29 71 data$ICU_3nRev = data$ICU_3n
data$ICU_3nRev = ifelse(data$ICU_3n == 1, 4, data$ICU_3nRev)
data$ICU_3nRev = ifelse(data$ICU_3n == 2, 3, data$ICU_3nRev)
data$ICU_3nRev = ifelse(data$ICU_3n == 3, 2, data$ICU_3nRev)
data$ICU_3nRev = ifelse(data$ICU_3n == 4, 1, data$ICU_3nRev)
table(data$ICU_3nRev)
1 2 3 4
71 29 3 1 table(data$ICU_17n)
2 3 4
6 45 55 data$ICU_17nRev = data$ICU_17n
data$ICU_17nRev = ifelse(data$ICU_17n == 1, 4, data$ICU_17nRev)
data$ICU_17nRev = ifelse(data$ICU_17n == 2, 3, data$ICU_17nRev)
data$ICU_17nRev = ifelse(data$ICU_17n == 3, 2, data$ICU_17nRev)
data$ICU_17nRev = ifelse(data$ICU_17n == 4, 1, data$ICU_17nRev)
table(data$ICU_17nRev)
1 2 3
55 45 6 table(data$ICU_24n)
1 2 3 4
6 25 44 31 data$ICU_24nRev = data$ICU_24n
data$ICU_24nRev = ifelse(data$ICU_24n == 1, 4, data$ICU_24nRev)
data$ICU_24nRev = ifelse(data$ICU_24n == 2, 3, data$ICU_24nRev)
data$ICU_24nRev = ifelse(data$ICU_24n == 3, 2, data$ICU_24nRev)
data$ICU_24nRev = ifelse(data$ICU_24n == 4, 1, data$ICU_24nRev)
table(data$ICU_24nRev)
1 2 3 4
31 44 25 6 table(data$ICU_13n)
1 2 3 4
7 46 43 10 data$ICU_13nRev = data$ICU_13n
data$ICU_13nRev = ifelse(data$ICU_13n == 1, 4, data$ICU_13nRev)
data$ICU_13nRev = ifelse(data$ICU_13n == 2, 3, data$ICU_13nRev)
data$ICU_13nRev = ifelse(data$ICU_13n == 3, 2, data$ICU_13nRev)
data$ICU_13nRev = ifelse(data$ICU_13n == 4, 1, data$ICU_13nRev)
table(data$ICU_13nRev)
1 2 3 4
10 43 46 7 table(data$ICU_5n)
1 2 3 4
4 16 42 44 data$ICU_5nRev = data$ICU_5n
data$ICU_5nRev = ifelse(data$ICU_5n == 1, 4, data$ICU_5nRev)
data$ICU_5nRev = ifelse(data$ICU_5n == 2, 3, data$ICU_5nRev)
data$ICU_5nRev = ifelse(data$ICU_5n == 3, 2, data$ICU_5nRev)
data$ICU_5nRev = ifelse(data$ICU_5n == 4, 1, data$ICU_5nRev)
table(data$ICU_5nRev)
1 2 3 4
44 42 16 4 # Unemotional
table(data$ICU_1n)
1 2 3 4
24 48 23 11 data$ICU_1nRev = data$ICU_1n
data$ICU_1nRev = ifelse(data$ICU_1n == 1, 4, data$ICU_1nRev)
data$ICU_1nRev = ifelse(data$ICU_1n == 2, 3, data$ICU_1nRev)
data$ICU_1nRev = ifelse(data$ICU_1n == 3, 2, data$ICU_1nRev)
data$ICU_1nRev = ifelse(data$ICU_1n == 4, 1, data$ICU_1nRev)
table(data$ICU_1nRev)
1 2 3 4
11 23 48 24 table(data$ICU_19n)
1 2 3 4
29 35 26 16 data$ICU_19nRev = data$ICU_19n
data$ICU_19nRev = ifelse(data$ICU_19n == 1, 4, data$ICU_19nRev)
data$ICU_19nRev = ifelse(data$ICU_19n == 2, 3, data$ICU_19nRev)
data$ICU_19nRev = ifelse(data$ICU_19n == 3, 2, data$ICU_19nRev)
data$ICU_19nRev = ifelse(data$ICU_19n == 4, 1, data$ICU_19nRev)
table(data$ICU_19nRev)
1 2 3 4
16 26 35 29 table(data$ICU_14n)
1 2 3 4
26 47 24 9 data$ICU_14nRev = data$ICU_14n
data$ICU_14nRev = ifelse(data$ICU_14n == 1, 4, data$ICU_14nRev)
data$ICU_14nRev = ifelse(data$ICU_14n == 2, 3, data$ICU_14nRev)
data$ICU_14nRev = ifelse(data$ICU_14n == 3, 2, data$ICU_14nRev)
data$ICU_14nRev = ifelse(data$ICU_14n == 4, 1, data$ICU_14nRev)
table(data$ICU_14nRev)
1 2 3 4
9 24 47 26 SSS
(1, 29, 32, 36, 5, 8, 24, 34, 39, 3, 16, 17, 28, 6, 9, 14, 18, 22)
Recoding was done by creating a “false object” or a place holder since it is a binary scale. Example below.
Diagram of recode
\[ A (OriginalValue) -> C(Placeholder) \] \[ B(OrginalValue) -> A(ReversedValue) \]
\[ C(Placeholder) -> B(ReverseValue) \]
# Disinhibition
table(data$ZSSS_1n)
0 1
22 84 data$ZSSS_1nRevFalse <- data$ZSSS_1n
data$ZSSS_1nRevFalse = ifelse(data$ZSSS_1n == 0, 2, data$ZSSS_1nRevFalse)
data$ZSSS_1nRevFalse = ifelse(data$ZSSS_1n == 1, 0, data$ZSSS_1nRevFalse)
table(data$ZSSS_1nRevFalse)
0 2
84 22 data$ZSSS_1nRev <- data$ZSSS_1nRevFalse
data$ZSSS_1nRev <- ifelse(data$ZSSS_1nRevFalse == 2, 1, data$ZSSS_1nRev)
table(data$ZSSS_1nRev)
0 1
84 22 table(data$ZSSS_29n)
0 1
19 86 data$ZSSS_29nRevFalse <- data$ZSSS_29n
data$ZSSS_29nRevFalse = ifelse(data$ZSSS_29n == 0, 2, data$ZSSS_29nRevFalse)
data$ZSSS_29nRevFalse = ifelse(data$ZSSS_29n == 1, 0, data$ZSSS_29nRevFalse)
table(data$ZSSS_29nRevFalse)
0 2
86 19 data$ZSSS_29nRev <- data$ZSSS_29nRevFalse
data$ZSSS_29nRev <- ifelse(data$ZSSS_29nRevFalse == 2, 1, data$ZSSS_29nRev)
table(data$ZSSS_29nRev)
0 1
86 19 table(data$ZSSS_32n)
0 1
65 41 data$ZSSS_32nRevFalse <- data$ZSSS_32n
data$ZSSS_32nRevFalse = ifelse(data$ZSSS_32n == 0, 2, data$ZSSS_32nRevFalse)
data$ZSSS_32nRevFalse = ifelse(data$ZSSS_32n == 1, 0, data$ZSSS_32nRevFalse)
table(data$ZSSS_32nRevFalse)
0 2
41 65 data$ZSSS_32nRev <- data$ZSSS_32nRevFalse
data$ZSSS_32nRev <- ifelse(data$ZSSS_32nRevFalse == 2, 1, data$ZSSS_32nRev)
table(data$ZSSS_32nRev)
0 1
41 65 table(data$ZSSS_36n)
0 1
41 63 data$ZSSS_36nRevFalse <- data$ZSSS_36n
data$ZSSS_36nRevFalse = ifelse(data$ZSSS_36n == 0, 2, data$ZSSS_36nRevFalse)
data$ZSSS_36nRevFalse = ifelse(data$ZSSS_36n == 1, 0, data$ZSSS_36nRevFalse)
table(data$ZSSS_36nRevFalse)
0 2
63 41 data$ZSSS_36nRev <- data$ZSSS_36nRevFalse
data$ZSSS_36nRev <- ifelse(data$ZSSS_36nRevFalse == 2, 1, data$ZSSS_36nRev)
table(data$ZSSS_36nRev)
0 1
63 41 # Boredom
table(data$ZSSS_5n)
0 1
12 94 data$ZSSS_5nRevFalse <- data$ZSSS_5n
data$ZSSS_5nRevFalse = ifelse(data$ZSSS_5n == 0, 2, data$ZSSS_5nRevFalse)
data$ZSSS_5nRevFalse = ifelse(data$ZSSS_5n == 1, 0, data$ZSSS_5nRevFalse)
table(data$ZSSS_5nRevFalse)
0 2
94 12 data$ZSSS_5nRev <- data$ZSSS_5nRevFalse
data$ZSSS_5nRev <- ifelse(data$ZSSS_5nRevFalse == 2, 1, data$ZSSS_5nRev)
table(data$ZSSS_5nRev)
0 1
94 12 table(data$ZSSS_8n)
0 1
31 75 data$ZSSS_8nRevFalse <- data$ZSSS_8n
data$ZSSS_8nRevFalse = ifelse(data$ZSSS_8n == 0, 2, data$ZSSS_8nRevFalse)
data$ZSSS_8nRevFalse = ifelse(data$ZSSS_8n == 1, 0, data$ZSSS_8nRevFalse)
table(data$ZSSS_8nRevFalse)
0 2
75 31 data$ZSSS_8nRev <- data$ZSSS_8nRevFalse
data$ZSSS_8nRev <- ifelse(data$ZSSS_8nRevFalse == 2, 1, data$ZSSS_8nRev)
table(data$ZSSS_8nRev)
0 1
75 31 table(data$ZSSS_24n)
0 1
24 82 data$ZSSS_24nRevFalse <- data$ZSSS_24n
data$ZSSS_24nRevFalse = ifelse(data$ZSSS_24n == 0, 2, data$ZSSS_24nRevFalse)
data$ZSSS_24nRevFalse = ifelse(data$ZSSS_24n == 1, 0, data$ZSSS_24nRevFalse)
table(data$ZSSS_24nRevFalse)
0 2
82 24 data$ZSSS_24nRev <- data$ZSSS_24nRevFalse
data$ZSSS_24nRev <- ifelse(data$ZSSS_24nRevFalse == 2, 1, data$ZSSS_24nRev)
table(data$ZSSS_24nRev)
0 1
82 24 table(data$ZSSS_34n)
0 1
33 73 data$ZSSS_34nRevFalse <- data$ZSSS_34n
data$ZSSS_34nRevFalse = ifelse(data$ZSSS_34n == 0, 2, data$ZSSS_34nRevFalse)
data$ZSSS_34nRevFalse = ifelse(data$ZSSS_34n == 1, 0, data$ZSSS_34nRevFalse)
table(data$ZSSS_34nRevFalse)
0 2
73 33 data$ZSSS_34nRev <- data$ZSSS_34nRevFalse
data$ZSSS_34nRev <- ifelse(data$ZSSS_34nRevFalse == 2, 1, data$ZSSS_34nRev)
table(data$ZSSS_34nRev)
0 1
73 33 table(data$ZSSS_39n)
0 1
26 80 data$ZSSS_39nRevFalse <- data$ZSSS_39n
data$ZSSS_39nRevFalse = ifelse(data$ZSSS_39n == 0, 2, data$ZSSS_39nRevFalse)
data$ZSSS_39nRevFalse = ifelse(data$ZSSS_39n == 1, 0, data$ZSSS_39nRevFalse)
table(data$ZSSS_39nRevFalse)
0 2
80 26 data$ZSSS_39nRev <- data$ZSSS_39nRevFalse
data$ZSSS_39nRev <- ifelse(data$ZSSS_39nRevFalse == 2, 1, data$ZSSS_39nRev)
table(data$ZSSS_39nRev)
0 1
80 26 # Thrill
table(data$ZSSS_3n)
0 1
61 45 data$ZSSS_3nRevFalse <- data$ZSSS_3n
data$ZSSS_3nRevFalse = ifelse(data$ZSSS_3n == 0, 2, data$ZSSS_3nRevFalse)
data$ZSSS_3nRevFalse = ifelse(data$ZSSS_3n == 1, 0, data$ZSSS_3nRevFalse)
table(data$ZSSS_3nRevFalse)
0 2
45 61 data$ZSSS_3nRev <- data$ZSSS_3nRevFalse
data$ZSSS_3nRev <- ifelse(data$ZSSS_3nRevFalse == 2, 1, data$ZSSS_3nRev)
table(data$ZSSS_3nRev)
0 1
45 61 table(data$ZSSS_16n)
0 1
67 39 data$ZSSS_16nRevFalse <- data$ZSSS_16n
data$ZSSS_16nRevFalse = ifelse(data$ZSSS_16n == 0, 2, data$ZSSS_16nRevFalse)
data$ZSSS_16nRevFalse = ifelse(data$ZSSS_16n == 1, 0, data$ZSSS_16nRevFalse)
table(data$ZSSS_16nRevFalse)
0 2
39 67 data$ZSSS_16nRev <- data$ZSSS_16nRevFalse
data$ZSSS_16nRev <- ifelse(data$ZSSS_16nRevFalse == 2, 1, data$ZSSS_16nRev)
table(data$ZSSS_16nRev)
0 1
39 67 table(data$ZSSS_17n)
0 1
80 26 data$ZSSS_17nRevFalse <- data$ZSSS_17n
data$ZSSS_17nRevFalse = ifelse(data$ZSSS_17n == 0, 2, data$ZSSS_17nRevFalse)
data$ZSSS_17nRevFalse = ifelse(data$ZSSS_17n == 1, 0, data$ZSSS_17nRevFalse)
table(data$ZSSS_17nRevFalse)
0 2
26 80 data$ZSSS_17nRev <- data$ZSSS_17nRevFalse
data$ZSSS_17nRev <- ifelse(data$ZSSS_17nRevFalse == 2, 1, data$ZSSS_17nRev)
table(data$ZSSS_17nRev)
0 1
26 80 table(data$ZSSS_23n)
0 1
70 36 data$ZSSS_23nRevFalse <- data$ZSSS_23n
data$ZSSS_23nRevFalse = ifelse(data$ZSSS_23n == 0, 2, data$ZSSS_23nRevFalse)
data$ZSSS_23nRevFalse = ifelse(data$ZSSS_23n == 1, 0, data$ZSSS_23nRevFalse)
table(data$ZSSS_23nRevFalse)
0 2
36 70 data$ZSSS_23nRev <- data$ZSSS_23nRevFalse
data$ZSSS_23nRev <- ifelse(data$ZSSS_23nRevFalse == 2, 1, data$ZSSS_23nRev)
table(data$ZSSS_23nRev)
0 1
36 70 table(data$ZSSS_28n)
0 1
45 60 data$ZSSS_28nRevFalse <- data$ZSSS_28n
data$ZSSS_28nRevFalse = ifelse(data$ZSSS_28n == 0, 2, data$ZSSS_28nRevFalse)
data$ZSSS_28nRevFalse = ifelse(data$ZSSS_28n == 1, 0, data$ZSSS_28nRevFalse)
table(data$ZSSS_28nRevFalse)
0 2
60 45 data$ZSSS_28nRev <- data$ZSSS_28nRevFalse
data$ZSSS_28nRev <- ifelse(data$ZSSS_28nRevFalse == 2, 1, data$ZSSS_28nRev)
table(data$ZSSS_28nRev)
0 1
60 45 # Exp
table(data$ZSSS_6n)
0 1
61 45 data$ZSSS_6nRevFalse <- data$ZSSS_6n
data$ZSSS_6nRevFalse = ifelse(data$ZSSS_6n == 0, 2, data$ZSSS_6nRevFalse)
data$ZSSS_6nRevFalse = ifelse(data$ZSSS_6n == 1, 0, data$ZSSS_6nRevFalse)
table(data$ZSSS_6nRevFalse)
0 2
45 61 data$ZSSS_6nRev <- data$ZSSS_6nRevFalse
data$ZSSS_6nRev <- ifelse(data$ZSSS_6nRevFalse == 2, 1, data$ZSSS_6nRev)
table(data$ZSSS_6nRev)
0 1
45 61 table(data$ZSSS_9n)
0 1
61 45 data$ZSSS_9nRevFalse <- data$ZSSS_9n
data$ZSSS_9nRevFalse = ifelse(data$ZSSS_9n == 0, 2, data$ZSSS_9nRevFalse)
data$ZSSS_9nRevFalse = ifelse(data$ZSSS_9n == 1, 0, data$ZSSS_9nRevFalse)
table(data$ZSSS_9nRevFalse)
0 2
45 61 data$ZSSS_9nRev <- data$ZSSS_9nRevFalse
data$ZSSS_9nRev <- ifelse(data$ZSSS_9nRevFalse == 2, 1, data$ZSSS_9nRev)
table(data$ZSSS_9nRev)
0 1
45 61 table(data$ZSSS_14n)
0 1
59 47 data$ZSSS_14nRevFalse <- data$ZSSS_14n
data$ZSSS_14nRevFalse = ifelse(data$ZSSS_14n == 0, 2, data$ZSSS_14nRevFalse)
data$ZSSS_14nRevFalse = ifelse(data$ZSSS_14n == 1, 0, data$ZSSS_14nRevFalse)
table(data$ZSSS_14nRevFalse)
0 2
47 59 data$ZSSS_14nRev <- data$ZSSS_14nRevFalse
data$ZSSS_14nRev <- ifelse(data$ZSSS_14nRevFalse == 2, 1, data$ZSSS_14nRev)
table(data$ZSSS_14nRev)
0 1
47 59 table(data$ZSSS_18n)
0 1
51 55 data$ZSSS_18nRevFalse <- data$ZSSS_18n
data$ZSSS_18nRevFalse = ifelse(data$ZSSS_18n == 0, 2, data$ZSSS_18nRevFalse)
data$ZSSS_18nRevFalse = ifelse(data$ZSSS_18n == 1, 0, data$ZSSS_18nRevFalse)
table(data$ZSSS_18nRevFalse)
0 2
55 51 data$ZSSS_18nRev <- data$ZSSS_18nRevFalse
data$ZSSS_18nRev <- ifelse(data$ZSSS_18nRevFalse == 2, 1, data$ZSSS_18nRev)
table(data$ZSSS_18nRev)
0 1
55 51 table(data$ZSSS_22n)
0 1
91 14 data$ZSSS_22nRevFalse <- data$ZSSS_22n
data$ZSSS_22nRevFalse = ifelse(data$ZSSS_22n == 0, 2, data$ZSSS_22nRevFalse)
data$ZSSS_22nRevFalse = ifelse(data$ZSSS_22n == 1, 0, data$ZSSS_22nRevFalse)
table(data$ZSSS_22nRevFalse)
0 2
14 91 data$ZSSS_22nRev <- data$ZSSS_22nRevFalse
data$ZSSS_22nRev <- ifelse(data$ZSSS_22nRevFalse == 2, 1, data$ZSSS_22nRev)
table(data$ZSSS_22nRev)
0 1
14 91 Scales
SRP
# SRP Tot
data$SRPTotalScore <- (data$SRP_01n + data$SRP_02n + data$SRP_03n + data$SRP_04n + data$SRP5nRev + data$SRP6nRev + data$SRP_07n +
data$SRP_08n + data$SRP_09n + data$SRP_10n + data$SRP11nRev + data$SRP_12n + data$SRP_13n + data$SRP14nRev +
data$SRP_15n + data$SRP16nRev + data$SRP_17n + data$SRP18nRev + data$SRP19nRev + data$SRP_20n + data$SRP21nRev +
data$SRP22nRev + data$SRP23nRev + data$SRP24nRev + data$SRP25nRev + data$SRP26nRev + data$SRP_27n + data$SRP_28n +
data$SRP_29n + data$SRP_30n + data$SRP31nRev + data$SRP_32n + data$SRP_33n + data$SRP34nRev + data$SRP_35n +
data$SRP36nRev + data$SRP_37n + data$SRP38nRev + data$SRP_39n + data$SRP_40n + data$SRP_41n + data$SRP_42n +
data$SRP_43n + data$SRP44nRev + data$SRP_45n + data$SRP46nRev + data$SRP47nRev + data$SRP_48n + data$SRP_49n +
data$SRP_50n + data$SRP_51n + data$SRP_52n + data$SRP_53n + data$SRP_54n + data$SRP_55n + data$SRP_56n +
data$SRP_57n + data$SRP_58n + data$SRP_59n + data$SRP_60n + data$SRP61nRev + data$SRP_62n + data$SRP_63n + data$SRP_64n)
#SRP IPM
data$SRPIPMTotal <- (data$SRP_03n + data$SRP_08n + data$SRP_13n + data$SRP16nRev + data$SRP_20n + data$SRP24nRev + data$SRP_27n + data$SRP31nRev +
data$SRP_35n + data$SRP38nRev + data$SRP_41n + data$SRP_45n + data$SRP_50n + data$SRP_54n + data$SRP_58n + data$SRP61nRev)
# SRP Callous
data$SRPCATotal <- (data$SRP_02n + data$SRP_07n + data$SRP11nRev + data$SRP_15n + data$SRP19nRev + data$SRP23nRev + data$SRP26nRev + data$SRP_30n + data$SRP_33n + data$SRP_37n + data$SRP_40n + data$SRP44nRev + data$SRP_48n + data$SRP_53n + data$SRP_56n + data$SRP_60n)
#SRP lifestyle
data$SRPELSTotal <- (data$SRP_01n + data$SRP_04n + data$SRP_09n + data$SRP14nRev + data$SRP_17n + data$SRP22nRev + data$SRP25nRev + data$SRP_28n + data$SRP_32n + data$SRP36nRev + data$SRP_39n + data$SRP_42n + data$SRP47nRev + data$SRP_51n +data$SRP_55n + data$SRP_59n)
# SRP Antisocial
data$SRPASBTotal <- (data$SRP5nRev + data$SRP6nRev + data$SRP_10n + data$SRP_12n + data$SRP18nRev + data$SRP21nRev + data$SRP_29n + data$SRP34nRev + data$SRP_43n + data$SRP46nRev + data$SRP_49n + data$SRP_52n + data$SRP_57n + data$SRP_62n + data$SRP_63n + data$SRP_64n)ICU
# ICU total
data$ICUTotScore <- (data$ICU_1nRev + data$ICU_2n + data$ICU_3nRev + data$ICU_4n + data$ICU_5nRev + data$ICU_6n +
data$ICU_7n + data$ICU_8nRev + data$ICU_9n + data$ICU_10n + data$ICU_11n + data$ICU_12n + data$ICU_13nRev +
data$ICU_14nRev + data$ICU_15nRev + data$ICU_16nRev + data$ICU_17nRev + data$ICU_18n + data$ICU_19nRev +
data$ICU_20n + data$ICU_21n + data$ICU_22n + data$ICU_23nRev + data$ICU_24nRev)
# ICU Cal
data$ICUCalTotalScore <- (data$ICU_4n + data$ICU_8nRev + data$ICU_9n + data$ICU_18n + data$ICU_11n + data$ICU_21n + data$ICU_7n + data$ICU_20n +
data$ICU_2n + data$ICU_12n + data$ICU_10n)
# ICU Uncare
data$ICUUncareTotalScore <- (data$ICU_15nRev + data$ICU_23nRev + data$ICU_16nRev + data$ICU_3nRev + data$ICU_17nRev + data$ICU_24nRev +
data$ICU_13nRev + data$ICU_5nRev)
# ICU Unemo
data$ICUUnemoTotal <- (data$ICU_1nRev + data$ICU_19nRev + data$ICU_6n + data$ICU_22n + data$ICU_14nRev)LSRP
# Total
data$LevTotalScore <- (data$Lev_01n + data$Lev_02n + data$Lev_03nRev + data$Lev_04n + data$Lev_05n + data$Lev_06n + data$Lev_07nRev + data$Lev_08n + data$Lev_09n + data$Lev_10nRev + data$Lev_11n + data$Lev_12n + data$Lev_13nRev + data$Lev_16n + data$Lev_17n + data$Lev_18n + data$Lev_19n + data$Lev_20n + data$Lev_21nRev + data$Lev_22n + data$Lev_23n + data$Lev_24n + data$Lev_25n + data$Lev_26nRev)
# Primary
data$LevPrimTotalScore <- (data$Lev_02n + data$Lev_04n + data$Lev_07nRev + data$Lev_09n + data$Lev_11n + data$Lev_12n + data$Lev_13nRev +
data$Lev_17n + data$Lev_19n + data$Lev_21nRev + data$Lev_22n + data$Lev_23n + data$Lev_24n + data$Lev_25n + data$Lev_26nRev)
# Seconnday
data$LevSecTotalScore <- (data$Lev_01n + data$Lev_03nRev + data$Lev_05n + data$Lev_06n + data$Lev_08n + data$Lev_10nRev + data$Lev_16n + data$Lev_18n + data$Lev_20n)ZSSS
# Total
data$SSSTotalScore <- (data$ZSSS_1nRev + data$ZSSS_2n + data$ZSSS_3nRev + data$ZSSS_4n + data$ZSSS_5nRev + data$ZSSS_6nRev + data$ZSSS_7n + data$ZSSS_8nRev + data$ZSSS_9nRev + data$ZSSS_10n + data$ZSSS_11n + data$ZSSS_12n + data$ZSSS_13n + data$ZSSS_14nRev + data$ZSSS_15n + data$ZSSS_16nRev + data$ZSSS_17nRev + data$ZSSS_18nRev + data$ZSSS_19n + data$ZSSS_20n + data$ZSSS_21n + data$ZSSS_22nRev + data$ZSSS_23nRev + data$ZSSS_24nRev + data$ZSSS_25n + data$ZSSS_26n + data$ZSSS_27n + data$ZSSS_28nRev + data$ZSSS_29nRev + data$ZSSS_30n + data$ZSSS_31n + data$ZSSS_32nRev + data$ZSSS_33n + data$ZSSS_34nRev + data$ZSSS_35n + data$ZSSS_36nRev + data$ZSSS_37n + data$ZSSS_38n + data$ZSSS_39nRev + data$ZSSS_40n)
# Disinhibited
data$SSSDISTotal <- (data$ZSSS_12n + data$ZSSS_13n + data$ZSSS_25n + data$ZSSS_30n + data$ZSSS_33n + data$ZSSS_35n +
data$ZSSS_1nRev + data$ZSSS_29nRev + data$ZSSS_32nRev + data$ZSSS_36nRev)
# Boredom
data$SSSBorTotal <- (data$ZSSS_2n + data$ZSSS_7n + data$ZSSS_15n + data$ZSSS_27n + data$ZSSS_31n + data$ZSSS_5nRev + data$ZSSS_8nRev + data$ZSSS_24nRev +
data$ZSSS_34nRev + data$ZSSS_39nRev)
# Thrill
data$SSSThrilTotal <- (data$ZSSS_11n + data$ZSSS_20n + data$ZSSS_21n + data$ZSSS_38n + data$ZSSS_40n + data$ZSSS_3nRev +
data$ZSSS_16nRev + data$ZSSS_17nRev + data$ZSSS_23nRev + data$ZSSS_28nRev)
# Exp
data$SSSExpTotal <- (data$ZSSS_4n + data$ZSSS_10n + data$ZSSS_19n + data$ZSSS_26n + data$ZSSS_37n + data$ZSSS_6nRev +
data$ZSSS_9nRev + data$ZSSS_14nRev + data$ZSSS_18nRev + data$ZSSS_22nRev) Autonomic Measures
Resting Heart Rate
data$HRbaseline <- (data$HRT_00_00 + data$HRT_00_01 + data$HRT_00_02 + data$HRT_00_03 + data$HRT_00_04 + data$HRT_00_05 + data$HRT_00_06 + data$HRT_00_07 + data$HRT_00_08 + data$HRT_00_09 + data$HRT_00_10 + data$HRT_00_11 + data$HRT_00_12 + data$HRT_00_13 + data$HRT_00_14 + data$HRT_00_15 + data$HRT_00_16 + data$HRT_00_17 + data$HRT_00_18 + data$HRT_00_19 + data$HRT_00_20 + data$HRT_00_21 + data$HRT_00_22 + data$HRT_00_23 + data$HRT_00_24 + data$HRT_00_25 + data$HRT_00_26 + data$HRT_00_27 + data$HRT_00_28 + data$HRT_00_29 + data$HRT_00_30 + data$HRT_00_31 + data$HRT_00_32 + data$HRT_00_33 + data$HRT_00_34 + data$HRT_00_35 + data$HRT_00_36 + data$HRT_00_37 + data$HRT_00_38 + data$HRT_00_39 + data$HRT_00_40 + data$HRT_00_41 + data$HRT_00_42 + data$HRT_00_43 + data$HRT_00_44 + data$HRT_00_45 + data$HRT_00_46 + data$HRT_00_47 + data$HRT_00_48 + data$HRT_00_49 + data$HRT_00_50 + data$HRT_00_51 + data$HRT_00_52 + data$HRT_00_53 + data$HRT_00_54 + data$HRT_00_55 + data$HRT_00_56 + data$HRT_00_57 + data$HRT_00_58 + data$HRT_00_59 + data$HRT_01_00 + data$HRT_01_01 + data$HRT_01_02 + data$HRT_01_03 + data$HRT_01_04 + data$HRT_01_05 + data$HRT_01_06 + data$HRT_01_07 + data$HRT_01_08 + data$HRT_01_09 + data$HRT_01_10 + data$HRT_01_11 + data$HRT_01_12 + data$HRT_01_13 + data$HRT_01_14 + data$HRT_01_15 + data$HRT_01_16 + data$HRT_01_17 + data$HRT_01_18 + data$HRT_01_19 + data$HRT_01_20 + data$HRT_01_21 + data$HRT_01_22 + data$HRT_01_23 + data$HRT_01_24 + data$HRT_01_25 + data$HRT_01_26 + data$HRT_01_27 + data$HRT_01_28 + data$HRT_01_29 + data$HRT_01_30 + data$HRT_01_31 + data$HRT_01_32 + data$HRT_01_33 + data$HRT_01_34 + data$HRT_01_35 + data$HRT_01_36 + data$HRT_01_37 + data$HRT_01_38 + data$HRT_01_39 + data$HRT_01_40 + data$HRT_01_41 + data$HRT_01_42 + data$HRT_01_43 + data$HRT_01_44 + data$HRT_01_45 + data$HRT_01_46 + data$HRT_01_47 + data$HRT_01_48 + data$HRT_01_49 + data$HRT_01_50 + data$HRT_01_51 + data$HRT_01_52 + data$HRT_01_53 + data$HRT_01_54 + data$HRT_01_55 + data$HRT_01_56 + data$HRT_01_57 + data$HRT_01_58 + data$HRT_01_59 + data$HRT_02_00 + data$HRT_02_01 + data$HRT_02_02 + data$HRT_02_03 + data$HRT_02_04 + data$HRT_02_05 + data$HRT_02_06 + data$HRT_02_07 + data$HRT_02_08 + data$HRT_02_09 + data$HRT_02_10 + data$HRT_02_11 + data$HRT_02_12 + data$HRT_02_13 + data$HRT_02_14 + data$HRT_02_15 + data$HRT_02_16 + data$HRT_02_17 + data$HRT_02_18 + data$HRT_02_19 + data$HRT_02_20 + data$HRT_02_21 + data$HRT_02_22 + data$HRT_02_23 + data$HRT_02_24 + data$HRT_02_25 + data$HRT_02_26 + data$HRT_02_27 + data$HRT_02_28 + data$HRT_02_29 + data$HRT_02_30 + data$HRT_02_31 + data$HRT_02_32 + data$HRT_02_33 + data$HRT_02_34 + data$HRT_02_35 + data$HRT_02_36 + data$HRT_02_37 + data$HRT_02_38 + data$HRT_02_39 + data$HRT_02_40 + data$HRT_02_41+ data$HRT_02_42 + data$HRT_02_43 + data$HRT_02_44 + data$HRT_02_45 + data$HRT_02_46 + data$HRT_02_47 + data$HRT_02_48 + data$HRT_02_49 + data$HRT_02_50 + data$HRT_02_51 + data$HRT_02_52 + data$HRT_02_53 + data$HRT_02_54 + data$HRT_02_55 + data$HRT_02_56+ data$HRT_02_57 + data$HRT_02_58 + data$HRT_02_59)/180Resting Skin Conductance
data$SCbaseline <- (data$SCT_00_00 + data$SCT_00_01 + data$SCT_00_02 + data$SCT_00_03 + data$SCT_00_04 + data$SCT_00_05 + data$SCT_00_06 + data$SCT_00_07 + data$SCT_00_08 + data$SCT_00_09 + data$SCT_00_10 + data$SCT_00_11 + data$SCT_00_12 + data$SCT_00_13 + data$SCT_00_14 + data$SCT_00_15 + data$SCT_00_16 + data$SCT_00_17 + data$SCT_00_18 + data$SCT_00_19 + data$SCT_00_20 + data$SCT_00_21 + data$SCT_00_22 + data$SCT_00_23 + data$SCT_00_24 + data$SCT_00_25 + data$SCT_00_26 + data$SCT_00_27 + data$SCT_00_28 + data$SCT_00_29 + data$SCT_00_30 + data$SCT_00_31 + data$SCT_00_32 + data$SCT_00_33 + data$SCT_00_34 + data$SCT_00_35 + data$SCT_00_36 + data$SCT_00_37 + data$SCT_00_38 + data$SCT_00_39 + data$SCT_00_40 + data$SCT_00_41 + data$SCT_00_42 + data$SCT_00_43 + data$SCT_00_44 + data$SCT_00_45 + data$SCT_00_46 + data$SCT_00_47 + data$SCT_00_48 + data$SCT_00_49 + data$SCT_00_50 + data$SCT_00_51 + data$SCT_00_52 + data$SCT_00_53 + data$SCT_00_54 + data$SCT_00_55 + data$SCT_00_56 + data$SCT_00_57 + data$SCT_00_58 + data$SCT_00_59 + data$SCT_01_00 + data$SCT_01_01 + data$SCT_01_02 + data$SCT_01_03 + data$SCT_01_04 + data$SCT_01_05 + data$SCT_01_06 + data$SCT_01_07 + data$SCT_01_08 + data$SCT_01_09 + data$SCT_01_10 + data$SCT_01_11 + data$SCT_01_12 + data$SCT_01_13 + data$SCT_01_14 + data$SCT_01_15 + data$SCT_01_16 + data$SCT_01_17 + data$SCT_01_18 + data$SCT_01_19 + data$SCT_01_20 + data$SCT_01_21 + data$SCT_01_22 + data$SCT_01_23 + data$SCT_01_24 + data$SCT_01_25 + data$SCT_01_26 + data$SCT_01_27 + data$SCT_01_28 + data$SCT_01_29 + data$SCT_01_30 + data$SCT_01_31 + data$SCT_01_32 + data$SCT_01_33 + data$SCT_01_34 + data$SCT_01_35 + data$SCT_01_36 + data$SCT_01_37 + data$SCT_01_38 + data$SCT_01_39 + data$SCT_01_40 + data$SCT_01_41 + data$SCT_01_42 + data$SCT_01_43 + data$SCT_01_44 + data$SCT_01_45 + data$SCT_01_46 + data$SCT_01_47 + data$SCT_01_48 + data$SCT_01_49 + data$SCT_01_50 + data$SCT_01_51 + data$SCT_01_52 + data$SCT_01_53 + data$SCT_01_54 + data$SCT_01_55 + data$SCT_01_56 + data$SCT_01_57 + data$SCT_01_58 + data$SCT_01_59 + data$SCT_02_00 + data$SCT_02_01 + data$SCT_02_02 + data$SCT_02_03 + data$SCT_02_04 + data$SCT_02_05 + data$SCT_02_06 + data$SCT_02_07 + data$SCT_02_08 + data$SCT_02_09 + data$SCT_02_10 + data$SCT_02_11 + data$SCT_02_12 + data$SCT_02_13 + data$SCT_02_14 + data$SCT_02_15 + data$SCT_02_16 + data$SCT_02_17 + data$SCT_02_18 + data$SCT_02_19 + data$SCT_02_20 + data$SCT_02_21 + data$SCT_02_22 + data$SCT_02_23 + data$SCT_02_24 + data$SCT_02_25 + data$SCT_02_26 + data$SCT_02_27 + data$SCT_02_28 + data$SCT_02_29 + data$SCT_02_30 + data$SCT_02_31 + data$SCT_02_32 + data$SCT_02_33 + data$SCT_02_34 + data$SCT_02_35 + data$SCT_02_36 + data$SCT_02_37 + data$SCT_02_38 + data$SCT_02_39 + data$SCT_02_40 + data$SCT_02_41 + data$SCT_02_42 + data$SCT_02_43 + data$SCT_02_44 + data$SCT_02_45 + data$SCT_02_46 + data$SCT_02_47 + data$SCT_02_48 + data$SCT_02_49 + data$SCT_02_50 + data$SCT_02_51 + data$SCT_02_52 + data$SCT_02_53 + data$SCT_02_54 + data$SCT_02_55 + data$SCT_02_56 + data$SCT_02_57 + data$SCT_02_58 + data$SCT_02_59)/180AUC Autonomic
The formula for the AUC code below can be found here.
AUC Countdown
HRR Signaled
data$CDHRCombAucgSigaled <- (data$HrStr_00_13 + data$HrStr_00_12)/2 + (data$HrStr_00_14 + data$HrStr_00_13)/2 + (data$HrStr_00_15 + data$HrStr_00_14)/2 + (data$HrStr_00_16 + data$HrStr_00_15)/2 + (data$HrStr_00_17 + data$HrStr_00_16)/2 + (data$HrStr_00_18 + data$HrStr_00_17)/2 + (data$HrStr_00_19 + data$HrStr_00_18)/2 + (data$HrStr_00_20 + data$HrStr_00_19)/2 + (data$HrStr_00_21 + data$HrStr_00_20)/2 + (data$HrStr_00_22 + data$HrStr_00_21)/2 + (data$HrStr_00_23 + data$HrStr_00_22) + (data$HrStr_00_26 + data$HrStr_00_25)/2 + (data$HrStr_00_27 + data$HrStr_00_26)/2 + (data$HrStr_00_28 + data$HrStr_00_27)/2 + (data$HrStr_00_29 + data$HrStr_00_28)/2 + (data$HrStr_00_30 + data$HrStr_00_29)/2 + (data$HrStr_00_31 + data$HrStr_00_30)/2 + (data$HrStr_00_32 + data$HrStr_00_31)/2 + (data$HrStr_00_33 + data$HrStr_00_32)/2 + (data$HrStr_00_34 + data$HrStr_00_33)/2 + (data$HrStr_00_35 + data$HrStr_00_34)/2 + (data$HrStr_00_36 + data$HrStr_00_35)/2 + (data$HrStr_00_37 + data$HrStr_00_36)/2 + (data$HrStr_00_38 + data$HrStr_00_37)/2 + (data$HrStr_00_39 + data$HrStr_00_38)/2 + (data$HrStr_00_40 + data$HrStr_00_39)/2 + (data$HrStr_00_41 + data$HrStr_00_40)/2 + (data$HrStr_00_42 + data$HrStr_00_41)/2 + (data$HrStr_00_43 + data$HrStr_00_42)/2 + (data$HrStr_00_44 + data$HrStr_00_43)/2 + (data$HrStr_01_43 + data$HrStr_01_42)/2 + (data$HrStr_01_44 + data$HrStr_01_43)/2 + (data$HrStr_01_45 + data$HrStr_01_44)/2 + (data$HrStr_01_46 + data$HrStr_01_45)/2 + (data$HrStr_01_47 + data$HrStr_01_46)/2 + (data$HrStr_01_48 + data$HrStr_01_47)/2 + (data$HrStr_01_49 + data$HrStr_01_48)/2 + (data$HrStr_01_50 + data$HrStr_01_49)/2 + (data$HrStr_01_51 + data$HrStr_01_50)/2 + (data$HrStr_01_52 + data$HrStr_01_51)/2 + (data$HrStr_01_53 + data$HrStr_01_52)/2 + (data$HrStr_01_56 + data$HrStr_01_55)/2 + (data$HrStr_01_57 + data$HrStr_01_56)/2 + (data$HrStr_01_58 + data$HrStr_01_57)/2 + (data$HrStr_01_59 + data$HrStr_01_58)/2 + (data$HrStr_02_00 + data$HrStr_01_59)/2 + (data$HrStr_02_01 + data$HrStr_02_00)/2 + (data$HrStr_02_02 + data$HrStr_02_01)/2 + (data$HrStr_02_03 + data$HrStr_02_02)/2 + (data$HrStr_02_04 + data$HrStr_02_03)/2 + (data$HrStr_02_05 + data$HrStr_02_04)/2 + (data$HrStr_02_06 + data$HrStr_02_05)/2 + (data$HrStr_02_07 + data$HrStr_02_06)/2 + (data$HrStr_02_08 + data$HrStr_02_07)/2 + (data$HrStr_02_09 + data$HrStr_02_08)/2 + (data$HrStr_02_10 + data$HrStr_02_09)/2 + (data$HrStr_02_11 + data$HrStr_02_12)/2 + (data$HrStr_02_12 + data$HrStr_02_11)/2 + (data$HrStr_02_13 + data$HrStr_02_12)/2 + (data$HrStr_02_14 + data$HrStr_02_13)/2
data$CDHRCombAuciSigaled <- data$CDHRCombAucgSigaled - (63 * data$HrStr_00_12)HRR Unsignaled
data$CDHRCombAucgUnSigaled <- (data$HrStr_00_58 + data$HrStr_00_57)/2 + (data$HrStr_00_59 + data$HrStr_00_58)/2 + (data$HrStr_01_00 + data$HrStr_00_59)/2 + (data$HrStr_01_01 + data$HrStr_01_00)/2 + (data$HrStr_01_02 + data$HrStr_01_01)/2 + (data$HrStr_01_03 + data$HrStr_01_02)/2 + (data$HrStr_01_04 + data$HrStr_01_03)/2 + (data$HrStr_01_05 + data$HrStr_01_04)/2 + (data$HrStr_01_06 + data$HrStr_01_05)/2 + (data$HrStr_01_07 + data$HrStr_01_06)/2 + (data$HrStr_01_08 + data$HrStr_01_07)/2 + (data$HrStr_01_11 + data$HrStr_01_10)/2 + (data$HrStr_01_12 + data$HrStr_01_11)/2 + (data$HrStr_01_13 + data$HrStr_01_12)/2 + (data$HrStr_01_14 + data$HrStr_01_13)/2 + (data$HrStr_01_15 + data$HrStr_01_14)/2 + (data$HrStr_01_16 + data$HrStr_01_15)/2 + (data$HrStr_01_17 + data$HrStr_01_16)/2 + (data$HrStr_01_18 + data$HrStr_01_17)/2 + (data$HrStr_01_19 + data$HrStr_01_18)/2 + (data$HrStr_01_20 + data$HrStr_01_19)/2 + (data$HrStr_01_21 + data$HrStr_01_20)/2 + (data$HrStr_01_22 + data$HrStr_01_21)/2 + (data$HrStr_01_23 + data$HrStr_01_22)/2 + (data$HrStr_01_24 + data$HrStr_01_23)/2 + (data$HrStr_01_25 + data$HrStr_01_24)/2 + (data$HrStr_01_26 + data$HrStr_01_25)/2 + (data$HrStr_01_27 + data$HrStr_01_26)/2 + (data$HrStr_01_28 + data$HrStr_01_27)/2 + (data$HrStr_01_29 + data$HrStr_01_28)/2 + (data$HrStr_02_28 + data$HrStr_02_27)/2 + (data$HrStr_02_29 + data$HrStr_02_28)/2 + (data$HrStr_02_29 + data$HrStr_02_28)/2 + (data$HrStr_02_30 + data$HrStr_02_29)/2 + (data$HrStr_02_31 + data$HrStr_02_30)/2 + (data$HrStr_02_32 + data$HrStr_02_31)/2 + (data$HrStr_02_33 + data$HrStr_02_32)/2 + (data$HrStr_02_34 + data$HrStr_02_33)/2 + (data$HrStr_02_35 + data$HrStr_02_34)/2 + (data$HrStr_02_36 + data$HrStr_02_35)/2 + (data$HrStr_02_37 + data$HrStr_02_36)/2 + (data$HrStr_02_38 + data$HrStr_02_37)/2 + (data$HrStr_02_41 + data$HrStr_02_40)/2 + (data$HrStr_02_42 + data$HrStr_02_41)/2 + (data$HrStr_02_43 + data$HrStr_02_42)/2 + (data$HrStr_02_44 + data$HrStr_02_43)/2 + (data$HrStr_02_45 + data$HrStr_02_44)/2 + (data$HrStr_02_46 + data$HrStr_02_45)/2 + (data$HrStr_02_47 + data$HrStr_02_46)/2 + (data$HrStr_02_48 + data$HrStr_02_47)/2 + (data$HrStr_02_49 + data$HrStr_02_48)/2 + (data$HrStr_02_50 + data$HrStr_02_49)/2 + (data$HrStr_02_51 + data$HrStr_02_50)/2 + (data$HrStr_02_52 + data$HrStr_02_51)/2 + (data$HrStr_02_53 + data$HrStr_02_52)/2 + (data$HrStr_02_54 + data$HrStr_02_53)/2 + (data$HrStr_02_55 + data$HrStr_02_54)/2 + (data$HrStr_02_56 + data$HrStr_02_55)/2 + (data$HrStr_02_57 + data$HrStr_02_56)/2 + (data$HrStr_02_58 + data$HrStr_02_57)/2 + (data$HrStr_02_59 + data$HrStr_02_58)/2
data$CDHRCombAuciUnSigaled <- data$CDHRCombAucgUnSigaled - (63 * data$HrStr_00_57)SC Signaled
data$CDSCCombAucgSigaled <- (data$ScStr_00_13 + data$ScStr_00_12)/2 + (data$ScStr_00_14 + data$ScStr_00_13)/2 + (data$ScStr_00_15 + data$ScStr_00_14)/2 + (data$ScStr_00_16 + data$ScStr_00_15)/2 + (data$ScStr_00_17 + data$ScStr_00_16)/2 + (data$ScStr_00_18 + data$ScStr_00_17)/2 + (data$ScStr_00_19 + data$ScStr_00_18)/2 + (data$ScStr_00_20 + data$ScStr_00_19)/2 + (data$ScStr_00_21 + data$ScStr_00_20)/2 + (data$ScStr_00_22 + data$ScStr_00_21)/2 + (data$ScStr_00_23 + data$ScStr_00_22)/2 + (data$ScStr_00_26 + data$ScStr_00_25)/2 + (data$ScStr_00_27 + data$ScStr_00_26)/2 + (data$ScStr_00_28 + data$ScStr_00_27)/2 + (data$ScStr_00_29 + data$ScStr_00_28)/2 + (data$ScStr_00_30 + data$ScStr_00_29)/2 + (data$ScStr_00_31 + data$ScStr_00_30)/2 + (data$ScStr_00_32 + data$ScStr_00_31)/2 + (data$ScStr_00_33 + data$ScStr_00_32)/2 + (data$ScStr_00_34 + data$ScStr_00_33)/2 + (data$ScStr_00_35 + data$ScStr_00_34)/2 + (data$ScStr_00_36 + data$ScStr_00_35)/2 + (data$ScStr_00_37 + data$ScStr_00_36)/2 + (data$ScStr_00_38 + data$ScStr_00_37)/2 + (data$ScStr_00_39 + data$ScStr_00_38)/2 + (data$ScStr_00_40 + data$ScStr_00_39)/2 + (data$ScStr_00_41 + data$ScStr_00_40)/2 + (data$ScStr_00_42 + data$ScStr_00_41)/2 + (data$ScStr_00_43 + data$ScStr_00_42)/2 + (data$ScStr_00_44 + data$ScStr_00_43)/2 + (data$ScStr_01_43 + data$ScStr_01_42)/2 + (data$ScStr_01_44 + data$ScStr_01_43)/2 + (data$ScStr_01_45 + data$ScStr_01_44)/2 + (data$ScStr_01_46 + data$ScStr_01_45)/2 + (data$ScStr_01_47 + data$ScStr_01_46)/2 + (data$ScStr_01_48 + data$ScStr_01_47)/2 + (data$ScStr_01_49 + data$ScStr_01_48)/2 + (data$ScStr_01_50 + data$ScStr_01_49)/2 + (data$ScStr_01_51 + data$ScStr_01_50)/2 + (data$ScStr_01_52 + data$ScStr_01_51)/2 + (data$ScStr_01_53 + data$ScStr_01_52)/2 + (data$ScStr_01_56 + data$ScStr_01_55)/2 + (data$ScStr_01_57 + data$ScStr_01_56)/2 + (data$ScStr_01_58 + data$ScStr_01_57)/2 + (data$ScStr_01_59 + data$ScStr_01_58)/2 + (data$ScStr_02_00 + data$ScStr_01_59)/2 + (data$ScStr_02_01 + data$ScStr_02_00)/2 + (data$ScStr_02_02 + data$ScStr_02_01)/2 + (data$ScStr_02_03 + data$ScStr_02_02)/2 + (data$ScStr_02_04 + data$ScStr_02_03)/2 + (data$ScStr_02_05 + data$ScStr_02_04)/2 + (data$ScStr_02_06 + data$ScStr_02_05)/2 + (data$ScStr_02_07 + data$ScStr_02_06)/2 + (data$ScStr_02_08 + data$ScStr_02_07)/2 + (data$ScStr_02_09 + data$ScStr_02_08)/2 + (data$ScStr_02_10 + data$ScStr_02_09)/2 + (data$ScStr_02_11 + data$ScStr_02_10)/2 + (data$ScStr_02_12 + data$ScStr_02_11)/2 + (data$ScStr_02_13 + data$ScStr_02_12)/2 + (data$ScStr_02_14 + data$ScStr_02_13)/2
data$CDSCCombAuciSigaled <- data$CDSCCombAucgSigaled - (63 * data$ScStr_00_12)SC Unsignaled
data$CDSCCombAucgUnSigaled <- (data$ScStr_00_58 + data$ScStr_00_57)/2 + (data$ScStr_00_59 + data$ScStr_00_58)/2 + (data$ScStr_01_00 + data$ScStr_00_59)/2 + (data$ScStr_01_01 + data$ScStr_01_00)/2 + (data$ScStr_01_02 + data$ScStr_01_01)/2 + (data$ScStr_01_03 + data$ScStr_01_02)/2 + (data$ScStr_01_04 + data$ScStr_01_03)/2 + (data$ScStr_01_05 + data$ScStr_01_04)/2 + (data$ScStr_01_06 + data$ScStr_01_05)/2 + (data$ScStr_01_07 + data$ScStr_01_06)/2 + (data$ScStr_01_08 + data$ScStr_01_07)/2 + (data$ScStr_01_11 + data$ScStr_01_10)/2 + (data$ScStr_01_12 + data$ScStr_01_11)/2 + (data$ScStr_01_13 + data$ScStr_01_12)/2 + (data$ScStr_01_14 + data$ScStr_01_13)/2 + (data$ScStr_01_15 + data$ScStr_01_14)/2 + (data$ScStr_01_16 + data$ScStr_01_15)/2 + (data$ScStr_01_17 + data$ScStr_01_16)/2 + (data$ScStr_01_18 + data$ScStr_01_17)/2 + (data$ScStr_01_19 + data$ScStr_01_18)/2 + (data$ScStr_01_20 + data$ScStr_01_19)/2 + (data$ScStr_01_21 + data$ScStr_01_20)/2 + (data$ScStr_01_22 + data$ScStr_01_21)/2 + (data$ScStr_01_23 + data$ScStr_01_22)/2 + (data$ScStr_01_24 + data$ScStr_01_23)/2 + (data$ScStr_01_25 + data$ScStr_01_24)/2 + (data$ScStr_01_26 + data$ScStr_01_25)/2 + (data$ScStr_01_27 + data$ScStr_01_26)/2 + (data$ScStr_01_28 + data$ScStr_01_27)/2 + (data$ScStr_01_29 + data$ScStr_01_28)/2 + (data$ScStr_02_28 + data$ScStr_02_27)/2 + (data$ScStr_02_29 + data$ScStr_02_28)/2 + (data$ScStr_02_30 + data$ScStr_02_29)/2 + (data$ScStr_02_31 + data$ScStr_02_30)/2 + (data$ScStr_02_32 + data$ScStr_02_31)/2 + (data$ScStr_02_33 + data$ScStr_02_32)/2 + (data$ScStr_02_34 + data$ScStr_02_33)/2 + (data$ScStr_02_35 + data$ScStr_02_34)/2 + (data$ScStr_02_36 + data$ScStr_02_35)/2 + (data$ScStr_02_37 + data$ScStr_02_36)/2 + (data$ScStr_02_38 + data$ScStr_02_37) + (data$ScStr_02_41 + data$ScStr_02_40)/2 + (data$ScStr_02_42 + data$ScStr_02_41)/2 + (data$ScStr_02_43 + data$ScStr_02_42)/2 + (data$ScStr_02_44 + data$ScStr_02_43)/2 + (data$ScStr_02_45 + data$ScStr_02_44)/2 + (data$ScStr_02_46 + data$ScStr_02_45)/2 + (data$ScStr_02_47 + data$ScStr_02_46)/2 + (data$ScStr_02_48 + data$ScStr_02_47)/2 + (data$ScStr_02_49 + data$ScStr_02_48)/2 + (data$ScStr_02_50 + data$ScStr_02_49)/2 + (data$ScStr_02_51 + data$ScStr_02_50)/2 + (data$ScStr_02_52 + data$ScStr_02_51)/2 + (data$ScStr_02_53 + data$ScStr_02_52)/2 + (data$ScStr_02_54 + data$ScStr_02_53)/2 + (data$ScStr_02_55 + data$ScStr_02_54)/2 + (data$ScStr_02_56 + data$ScStr_02_55)/2 + (data$ScStr_02_57 + data$ScStr_02_56)/2 + (data$ScStr_02_58 + data$ScStr_02_57)/2 + (data$ScStr_02_59 + data$ScStr_02_58)/2
data$CDSCCombAuciUnSigaled <- data$CDSCCombAucgUnSigaled - (63 * data$ScStr_00_57)Wrangling
Full Sample
Table 1
#Full
FullsampleFinalSurveyT1 <- data |>
dplyr::select(Task, Gender, race_eth, race_eth2, White, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal)
FSFSurveyT1 <- FullsampleFinalSurveyT1 |>
na.omit()
FullsampleFinalHRT1 <- data |>
dplyr::select(Task, Gender, race_eth, race_eth2, White, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, HRbaseline)
FSFHRT1 <- FullsampleFinalHRT1 |>
na.omit()
FullsampleFinalSCT1 <- data |>
dplyr::select(Task, Gender, race_eth, race_eth2, White, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SCbaseline)
FSFSCT1 <- FullsampleFinalSCT1 |>
na.omit()
# Social Stressor
SocialStressorFinalHRT1 <- data |>
dplyr::select(Task, White, Gender, Male, Female, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SSHRCombAUCi, HRbaseline) |>
filter(Task == "2")
SSFHRT1 <- SocialStressorFinalHRT1 |>
na.omit()
SocialStressorFinalSCT1 <- data |>
dplyr::select(Task, White, Gender, Male, Female, Age, GenderNumb,SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SSSCCombAUCi, SCbaseline) |>
filter(Task == "2")
SSFSCT1 <- SocialStressorFinalSCT1 |>
na.omit()
# Countdown
CountdownFinalHRT1 <- data |>
dplyr::select(Task, Male, Female, White, Gender, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, CDHRCombAuciSigaled, CDHRCombAuciUnSigaled, HRbaseline) |>
filter(Task == "1")
CDFHRT1 <- CountdownFinalHRT1 |>
na.omit()
CountdownFinalSCT1 <- data |>
dplyr::select(Task, Male, Female, White, Gender, Age, GenderNumb, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal, SSSThrilTotal, SSSExpTotal,
CDSCCombAuciSigaled, CDSCCombAuciUnSigaled, SCbaseline) |>
filter(Task == "1")
CDFSCT1 <- CountdownFinalSCT1 |>
na.omit()Male Only
Table 1
These data frames were required to compensate for the missing variables. If I just selected the one column I needed (e.g.,“HRbaseline”) the missing would not match the true sample number because missing values are contained within the survey. This is most evident in the female sample (Female Only Table 1 code chunk).
# Baseline
MaleHRbaseT1 <- data |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, HRbaseline) |>
filter(GenderNumb == "2")
MHRbT1 <- MaleHRbaseT1 |>
na.omit()
MaleSCbaseT1 <- data |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SCbaseline) |>
filter(GenderNumb == "2")
MSCbT1 <- MaleSCbaseT1 |>
na.omit()
# Social Stressor
MaleSSHRT1 <- SocialStressorFinalHRT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SSHRCombAUCi) |>
filter(GenderNumb == "2")
MSSHRT1 <- MaleSSHRT1 |>
na.omit()
MaleSSSCT1 <- SocialStressorFinalSCT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SSSCCombAUCi) |>
filter(GenderNumb == "2")
MSSSCT1 <- MaleSSSCT1 |>
na.omit()
# Countdown
MaleCDHRT1 <- CountdownFinalHRT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, CDHRCombAuciSigaled, CDHRCombAuciUnSigaled) |>
filter(GenderNumb == "2")
MCDHRT1 <- MaleCDHRT1 |>
na.omit()
MaleCDSCT1 <- CountdownFinalSCT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, CDSCCombAuciSigaled, CDSCCombAuciUnSigaled) |>
filter(GenderNumb == "2")
MCDSCT1 <- MaleCDSCT1 |>
na.omit()Female Only
Distribution Checks
# Survey only for distribution checks
FemaleDistribCheck <- data |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal) |>
filter(GenderNumb == "1")
FemaleDisCheck <- FemaleDistribCheck |>
na.omit()Table 1
# baseline
FemaleHRbaselineT1 <- data |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, HRbaseline) |>
filter(GenderNumb == "1")
FemaleHRbaseT1 <- FemaleHRbaselineT1 |>
na.omit()
FemaleSCbaselineT1 <- data |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SCbaseline) |>
filter(GenderNumb == "1")
FemaleSCbaseT1 <- FemaleSCbaselineT1 |>
na.omit()
# Social Stressor
FemaleSocialSHRT1 <- SocialStressorFinalHRT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SSHRCombAUCi) |>
filter(GenderNumb == "1")
FemaleSSHRT1 <- FemaleSocialSHRT1 |>
na.omit()
FemaleSocialSSCT1 <- SocialStressorFinalSCT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, SSSCCombAUCi) |>
filter(GenderNumb == "1")
FemaleSCSST1 <- FemaleSocialSSCT1 |>
na.omit()
# Countdown
FemaleHRCountDT1 <- CountdownFinalHRT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, CDHRCombAuciSigaled, CDHRCombAuciUnSigaled) |>
filter(GenderNumb == "1")
FemaleHRCDT1 <- FemaleHRCountDT1 |>
na.omit()
FemaleSCCountDT1 <- CountdownFinalSCT1 |>
dplyr::select(GenderNumb, White, Age, SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal, CDSCCombAuciSigaled, CDSCCombAuciUnSigaled) |>
filter(GenderNumb == "1")
FemaleSCCDT1 <- FemaleSCCountDT1 |>
na.omit()Scale Reliability Data Frame
Created a new data frame that takes into account the sample and the missing data to calculate the alphas. Can’t use original data frome due to the nature of the autonomic data (i.e., contains a varying amount NAs for all participants, therefore can’t na.omit). To save time and reduce error, I used gsub(). Process below to replicate.
Code <- “Copy and paste the code from the total scores”
new_code <- gsub(“data\$”, ““, code) ^ use an escape character (i.e., \) to treat the $ as normal character and not a special expression. Throws in a”” spot.
new_code2 <- gsub(“\+”, “,”, new_code) ^ same story here.
cat(new_code2) ^ Concatenate the variables into a neat string and copy from console to code chunk.
Scaledf <- data |>
dplyr::select (SRP_01n , SRP_02n , SRP_03n , SRP_04n , SRP5nRev , SRP6nRev , SRP_07n , SRP_08n ,
SRP_09n , SRP_10n , SRP11nRev , SRP_12n , SRP_13n , SRP14nRev ,
SRP_15n , SRP16nRev , SRP_17n , SRP18nRev , SRP19nRev , SRP_20n , SRP21nRev ,
SRP22nRev , SRP23nRev , SRP24nRev , SRP25nRev , SRP26nRev , SRP_27n , SRP_28n ,
SRP_29n , SRP_30n , SRP31nRev , SRP_32n , SRP_33n , SRP34nRev , SRP_35n ,
SRP36nRev , SRP_37n , SRP38nRev , SRP_39n , SRP_40n , SRP_41n , SRP_42n ,
SRP_43n , SRP44nRev , SRP_45n , SRP46nRev , SRP47nRev , SRP_48n , SRP_49n ,
SRP_50n , SRP_51n , SRP_52n , SRP_53n , SRP_54n , SRP_55n , SRP_56n ,
SRP_57n , SRP_58n , SRP_59n , SRP_60n , SRP61nRev , SRP_62n , SRP_63n , SRP_64n ,
ICU_1nRev , ICU_2n , ICU_3nRev , ICU_4n , ICU_5nRev , ICU_6n ,
ICU_7n , ICU_8nRev , ICU_9n , ICU_10n , ICU_11n , ICU_12n , ICU_13nRev ,
ICU_14nRev , ICU_15nRev , ICU_16nRev , ICU_17nRev , ICU_18n , ICU_19nRev ,
ICU_20n , ICU_21n , ICU_22n , ICU_23nRev , ICU_24nRev ,
Lev_01n , Lev_02n , Lev_03nRev , Lev_04n , Lev_05n , Lev_06n , Lev_07nRev , Lev_08n ,
Lev_09n , Lev_10nRev , Lev_11n , Lev_12n , Lev_13nRev , Lev_16n , Lev_17n , Lev_18n ,
Lev_19n , Lev_20n , Lev_21nRev , Lev_22n , Lev_23n , Lev_24n , Lev_25n , Lev_26nRev ,
ZSSS_1nRev , ZSSS_2n , ZSSS_3nRev , ZSSS_4n , ZSSS_5nRev , ZSSS_6nRev , ZSSS_7n , ZSSS_8nRev ,
ZSSS_9nRev , ZSSS_10n , ZSSS_11n , ZSSS_12n , ZSSS_13n , ZSSS_14nRev , ZSSS_15n , ZSSS_16nRev ,
ZSSS_17nRev , ZSSS_18nRev , ZSSS_19n , ZSSS_20n , ZSSS_21n , ZSSS_22nRev , ZSSS_23nRev , ZSSS_24nRev ,
ZSSS_25n , ZSSS_26n , ZSSS_27n , ZSSS_28nRev , ZSSS_29nRev , ZSSS_30n , ZSSS_31n ,
ZSSS_32nRev , ZSSS_33n , ZSSS_34nRev , ZSSS_35n , ZSSS_36nRev , ZSSS_37n , ZSSS_38n , ZSSS_39nRev , ZSSS_40n) |>
na.omit()
# SRP
SRPTotA <-Scaledf[ , c("SRP_03n","SRP_08n","SRP_13n","SRP16nRev","SRP_20n","SRP24nRev","SRP_27n",
"SRP31nRev","SRP_35n", "SRP38nRev","SRP_41n", "SRP_45n","SRP_50n", "SRP_54n","SRP_58n", "SRP61nRev",
"SRP_02n","SRP_07n", "SRP11nRev", "SRP_15n", "SRP19nRev", "SRP23nRev", "SRP26nRev", "SRP_30n", "SRP_33n",
"SRP_37n", "SRP_40n", "SRP44nRev", "SRP_48n", "SRP_01n", "SRP_53n", "SRP_56n", "SRP_60n", "SRP_04n",
"SRP_09n", "SRP14nRev", "SRP_17n", "SRP22nRev", "SRP25nRev", "SRP_28n", "SRP_32n", "SRP36nRev", "SRP_39n",
"SRP_42n", "SRP47nRev", "SRP_51n", "SRP_55n", "SRP_59n", "SRP5nRev", "SRP6nRev", "SRP_10n", "SRP_12n",
"SRP18nRev", "SRP21nRev", "SRP_29n", "SRP34nRev", "SRP_43n", "SRP46nRev", "SRP_49n", "SRP_52n",
"SRP_57n", "SRP_62n", "SRP_63n", "SRP_64n")]
SRPIPMA <-Scaledf[ , c("SRP_03n","SRP_08n","SRP_13n","SRP16nRev","SRP_20n","SRP24nRev","SRP_27n",
"SRP31nRev","SRP_35n", "SRP38nRev","SRP_41n", "SRP_45n","SRP_50n", "SRP_54n","SRP_58n", "SRP61nRev")]
SRPICAA <-Scaledf[ , c("SRP_02n","SRP_07n","SRP11nRev","SRP_15n","SRP19nRev","SRP23nRev","SRP26nRev",
"SRP_30n","SRP_33n", "SRP_37n","SRP_40n", "SRP44nRev","SRP_48n", "SRP_53n","SRP_56n", "SRP_60n")]
SRPELSA <-Scaledf[ , c("SRP_01n","SRP_04n","SRP_09n","SRP14nRev","SRP_17n","SRP22nRev","SRP25nRev",
"SRP_28n","SRP_32n", "SRP36nRev","SRP_39n", "SRP_42n","SRP47nRev", "SRP_51n","SRP_55n", "SRP_59n")]
SRPASBA <-Scaledf[ , c("SRP5nRev","SRP6nRev","SRP_10n","SRP_12n","SRP18nRev","SRP21nRev","SRP_29n",
"SRP34nRev","SRP_43n", "SRP46nRev","SRP_49n", "SRP_52n","SRP_57n", "SRP_62n","SRP_63n", "SRP_64n")]
# ICU
ICUTotA <-Scaledf[ , c("ICU_1nRev","ICU_2n","ICU_3nRev","ICU_4n","ICU_5nRev","ICU_6n","ICU_7n",
"ICU_8nRev","ICU_9n", "ICU_10n", "ICU_11n","ICU_12n", "ICU_13nRev","ICU_14nRev", "ICU_15nRev",
"ICU_16nRev","ICU_17nRev", "ICU_18n","ICU_19nRev", "ICU_20n","ICU_21n", "ICU_22n","ICU_23nRev", "ICU_24nRev")]
ICUCalA <-Scaledf[ , c("ICU_4n","ICU_8nRev","ICU_9n","ICU_18n","ICU_11n","ICU_21n","ICU_7n",
"ICU_20n","ICU_2n", "ICU_12n","ICU_10n")]
ICUUncareA <-Scaledf[ , c("ICU_15nRev","ICU_23nRev","ICU_16nRev","ICU_3nRev","ICU_17nRev","ICU_24nRev","ICU_13nRev",
"ICU_5nRev")]
ICUUnemoA <-Scaledf[ , c("ICU_1nRev","ICU_19nRev","ICU_6n","ICU_22n","ICU_14nRev")]
# LSRP
LevTotA <-Scaledf[ , c("Lev_01n","Lev_02n","Lev_03nRev","Lev_04n","Lev_05n","Lev_06n","Lev_07nRev",
"Lev_08n","Lev_09n", "Lev_10nRev","Lev_11n","Lev_12n", "Lev_13nRev","Lev_16n","Lev_17n", "Lev_18n",
"Lev_19n", "Lev_20n","Lev_21nRev", "Lev_22n","Lev_23n", "Lev_24n", "Lev_25n","Lev_26nRev" )]
LevPrimA <-Scaledf[ , c("Lev_02n","Lev_04n","Lev_07nRev","Lev_09n","Lev_11n","Lev_12n",
"Lev_13nRev","Lev_17n", "Lev_19n","Lev_21nRev","Lev_22n", "Lev_23n","Lev_24n","Lev_25n", "Lev_26nRev")]
LevSecA <-Scaledf[ , c("Lev_01n","Lev_03nRev","Lev_05n","Lev_06n","Lev_08n","Lev_10nRev",
"Lev_16n","Lev_18n", "Lev_20n")]
# SSS
SSSTotA <-Scaledf[ , c("ZSSS_1nRev","ZSSS_2n","ZSSS_3nRev","ZSSS_4n","ZSSS_5nRev","ZSSS_6nRev","ZSSS_7n", "ZSSS_8nRev","ZSSS_9nRev", "ZSSS_10n",
"ZSSS_11n", "ZSSS_12n","ZSSS_13n", "ZSSS_14nRev","ZSSS_15n", "ZSSS_16nRev", "ZSSS_17nRev","ZSSS_18nRev", "ZSSS_19n", "ZSSS_20n",
"ZSSS_21n", "ZSSS_22nRev", "ZSSS_23nRev", "ZSSS_24nRev", "ZSSS_25n", "ZSSS_26n", "ZSSS_27n","ZSSS_28nRev","ZSSS_29nRev","ZSSS_30n","ZSSS_31n",
"ZSSS_32nRev","ZSSS_33n","ZSSS_34nRev","ZSSS_35n","ZSSS_36nRev","ZSSS_37n","ZSSS_38n", "ZSSS_39nRev", "ZSSS_40n")]
SSSDISA <-Scaledf[ , c("ZSSS_12n","ZSSS_13n","ZSSS_25n","ZSSS_30n","ZSSS_33n","ZSSS_35n",
"ZSSS_1nRev","ZSSS_29nRev", "ZSSS_32nRev", "ZSSS_36nRev")]
SSSBorA <-Scaledf[ , c("ZSSS_2n","ZSSS_7n","ZSSS_15n","ZSSS_27n","ZSSS_31n","ZSSS_5nRev",
"ZSSS_8nRev","ZSSS_24nRev", "ZSSS_34nRev", "ZSSS_39nRev")]
SSSThrilA <-Scaledf[ , c("ZSSS_11n","ZSSS_20n","ZSSS_21n","ZSSS_38n","ZSSS_40n","ZSSS_3nRev",
"ZSSS_16nRev","ZSSS_17nRev", "ZSSS_23nRev", "ZSSS_28nRev")]
SSSExpA <-Scaledf[ , c("ZSSS_4n","ZSSS_10n","ZSSS_19n","ZSSS_26n","ZSSS_37n","ZSSS_6nRev",
"ZSSS_9nRev","ZSSS_14nRev", "ZSSS_18nRev", "ZSSS_22nRev")]Analysis
Reliabity Scores
# SRP
cronbach.alpha(SRPTotA)
Cronbach's alpha for the 'SRPTotA' data-set
Items: 64
Sample units: 92
alpha: 0.884cronbach.alpha(SRPIPMA)
Cronbach's alpha for the 'SRPIPMA' data-set
Items: 16
Sample units: 92
alpha: 0.797cronbach.alpha(SRPICAA)
Cronbach's alpha for the 'SRPICAA' data-set
Items: 16
Sample units: 92
alpha: 0.752cronbach.alpha(SRPELSA)
Cronbach's alpha for the 'SRPELSA' data-set
Items: 16
Sample units: 92
alpha: 0.788cronbach.alpha(SRPASBA)
Cronbach's alpha for the 'SRPASBA' data-set
Items: 16
Sample units: 92
alpha: 0.713# ICU
cronbach.alpha(ICUTotA)
Cronbach's alpha for the 'ICUTotA' data-set
Items: 24
Sample units: 92
alpha: 0.802cronbach.alpha(ICUCalA)
Cronbach's alpha for the 'ICUCalA' data-set
Items: 11
Sample units: 92
alpha: 0.395cronbach.alpha(ICUUncareA)
Cronbach's alpha for the 'ICUUncareA' data-set
Items: 8
Sample units: 92
alpha: 0.778cronbach.alpha(ICUUnemoA)
Cronbach's alpha for the 'ICUUnemoA' data-set
Items: 5
Sample units: 92
alpha: 0.888# LSRP
cronbach.alpha(LevTotA)
Cronbach's alpha for the 'LevTotA' data-set
Items: 24
Sample units: 92
alpha: 0.827cronbach.alpha(LevPrimA)
Cronbach's alpha for the 'LevPrimA' data-set
Items: 15
Sample units: 92
alpha: 0.804cronbach.alpha(LevSecA)
Cronbach's alpha for the 'LevSecA' data-set
Items: 9
Sample units: 92
alpha: 0.664# ZSSS
cronbach.alpha(SSSTotA)
Cronbach's alpha for the 'SSSTotA' data-set
Items: 40
Sample units: 92
alpha: 0.751cronbach.alpha(SSSDISA)
Cronbach's alpha for the 'SSSDISA' data-set
Items: 10
Sample units: 92
alpha: 0.674cronbach.alpha(SSSBorA)
Cronbach's alpha for the 'SSSBorA' data-set
Items: 10
Sample units: 92
alpha: 0.483cronbach.alpha(SSSThrilA)
Cronbach's alpha for the 'SSSThrilA' data-set
Items: 10
Sample units: 92
alpha: 0.8cronbach.alpha(SSSExpA)
Cronbach's alpha for the 'SSSExpA' data-set
Items: 10
Sample units: 92
alpha: 0.425Table 1 (Descriptives)
Survey Means
# full
FSDescriptives <- FSFSurveyT1 |>
summarise(
across(
.cols = c(SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal, ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore, LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,SSSThrilTotal, SSSExpTotal
),
.fns = c( # this is used to describe the function within a list of the output (i.e., mean and sd)
mean = \(x) mean(x, na.rm = T),
sd = \(x) sd(x, na.rm =T)
),
.names = '{.col} ---- {.fn}'
)
) |>
pivot_longer(
cols = everything()
)
knitr::kable(FSDescriptives)| name | value |
|---|---|
| SRPTotalScore —- mean | 141.554348 |
| SRPTotalScore —- sd | 22.906098 |
| SRPIPMTotal —- mean | 37.684783 |
| SRPIPMTotal —- sd | 7.609897 |
| SRPCATotal —- mean | 36.945652 |
| SRPCATotal —- sd | 7.564178 |
| SRPELSTotal —- mean | 42.163044 |
| SRPELSTotal —- sd | 8.894099 |
| SRPASBTotal —- mean | 24.760870 |
| SRPASBTotal —- sd | 6.947795 |
| ICUTotScore —- mean | 42.717391 |
| ICUTotScore —- sd | 7.124214 |
| ICUCalTotalScore —- mean | 15.532609 |
| ICUCalTotalScore —- sd | 2.160846 |
| ICUUncareTotalScore —- mean | 14.315217 |
| ICUUncareTotalScore —- sd | 3.673275 |
| ICUUnemoTotal —- mean | 12.869565 |
| ICUUnemoTotal —- sd | 3.911711 |
| LevTotalScore —- mean | 46.532609 |
| LevTotalScore —- sd | 7.609206 |
| LevPrimTotalScore —- mean | 28.217391 |
| LevPrimTotalScore —- sd | 5.232666 |
| LevSecTotalScore —- mean | 18.315217 |
| LevSecTotalScore —- sd | 3.563951 |
| SSSTotalScore —- mean | 17.119565 |
| SSSTotalScore —- sd | 5.516892 |
| SSSDISTotal —- mean | 3.978261 |
| SSSDISTotal —- sd | 2.362613 |
| SSSBorTotal —- mean | 2.076087 |
| SSSBorTotal —- sd | 1.665643 |
| SSSThrilTotal —- mean | 6.086957 |
| SSSThrilTotal —- sd | 2.884792 |
| SSSExpTotal —- mean | 4.978261 |
| SSSExpTotal —- sd | 1.827616 |
# female
FSDescriptivesFemale <- FSFSurveyT1 |>
filter(GenderNumb == "1") |>
summarise(
across(
.cols = c(SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal
),
.fns = c(
mean = \(x) mean(x, na.rm = T),
sd = \(x) sd(x, na.rm =T)
),
.names = '{.col} ---- {.fn}'
)
) |>
pivot_longer(
cols = everything()
)
knitr::kable(FSDescriptivesFemale)| name | value |
|---|---|
| SRPTotalScore —- mean | 138.985916 |
| SRPTotalScore —- sd | 23.912634 |
| SRPIPMTotal —- mean | 37.126761 |
| SRPIPMTotal —- sd | 8.095730 |
| SRPCATotal —- mean | 35.295775 |
| SRPCATotal —- sd | 6.776840 |
| SRPELSTotal —- mean | 41.915493 |
| SRPELSTotal —- sd | 9.401742 |
| SRPASBTotal —- mean | 24.647887 |
| SRPASBTotal —- sd | 6.911892 |
| ICUTotScore —- mean | 41.661972 |
| ICUTotScore —- sd | 7.197110 |
| ICUCalTotalScore —- mean | 15.211268 |
| ICUCalTotalScore —- sd | 1.948740 |
| ICUUncareTotalScore —- mean | 14.070422 |
| ICUUncareTotalScore —- sd | 3.896240 |
| ICUUnemoTotal —- mean | 12.380282 |
| ICUUnemoTotal —- sd | 3.822363 |
| LevTotalScore —- mean | 45.788732 |
| LevTotalScore —- sd | 7.882921 |
| LevPrimTotalScore —- mean | 27.591549 |
| LevPrimTotalScore —- sd | 5.172945 |
| LevSecTotalScore —- mean | 18.197183 |
| LevSecTotalScore —- sd | 3.804583 |
| SSSTotalScore —- mean | 16.774648 |
| SSSTotalScore —- sd | 5.695104 |
| SSSDISTotal —- mean | 4.000000 |
| SSSDISTotal —- sd | 2.420154 |
| SSSBorTotal —- mean | 1.971831 |
| SSSBorTotal —- sd | 1.698502 |
| SSSThrilTotal —- mean | 5.718310 |
| SSSThrilTotal —- sd | 2.889306 |
| SSSExpTotal —- mean | 5.084507 |
| SSSExpTotal —- sd | 1.688165 |
# male
FSDescriptivesMale <- FSFSurveyT1 |>
filter(GenderNumb == "2") |>
summarise(
across(
.cols = c(SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal, SRPASBTotal,
ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal,LevTotalScore,
LevPrimTotalScore, LevSecTotalScore, SSSTotalScore,SSSDISTotal, SSSBorTotal,
SSSThrilTotal, SSSExpTotal
),
.fns = c(
mean = \(x) mean(x, na.rm = T),
sd = \(x) sd(x, na.rm =T)
),
.names = '{.col} ---- {.fn}'
)
) |>
pivot_longer(
cols = everything()
)
knitr::kable(FSDescriptivesMale)| name | value |
|---|---|
| SRPTotalScore —- mean | 150.238095 |
| SRPTotalScore —- sd | 16.834205 |
| SRPIPMTotal —- mean | 39.571429 |
| SRPIPMTotal —- sd | 5.408987 |
| SRPCATotal —- mean | 42.523809 |
| SRPCATotal —- sd | 7.567160 |
| SRPELSTotal —- mean | 43.000000 |
| SRPELSTotal —- sd | 7.042727 |
| SRPASBTotal —- mean | 25.142857 |
| SRPASBTotal —- sd | 7.226934 |
| ICUTotScore —- mean | 46.285714 |
| ICUTotScore —- sd | 5.684566 |
| ICUCalTotalScore —- mean | 16.619048 |
| ICUCalTotalScore —- sd | 2.519448 |
| ICUUncareTotalScore —- mean | 15.142857 |
| ICUUncareTotalScore —- sd | 2.707133 |
| ICUUnemoTotal —- mean | 14.523810 |
| ICUUnemoTotal —- sd | 3.842122 |
| LevTotalScore —- mean | 49.047619 |
| LevTotalScore —- sd | 6.111270 |
| LevPrimTotalScore —- mean | 30.333333 |
| LevPrimTotalScore —- sd | 4.983306 |
| LevSecTotalScore —- mean | 18.714286 |
| LevSecTotalScore —- sd | 2.629503 |
| SSSTotalScore —- mean | 18.285714 |
| SSSTotalScore —- sd | 4.807732 |
| SSSDISTotal —- mean | 3.904762 |
| SSSDISTotal —- sd | 2.211442 |
| SSSBorTotal —- mean | 2.428571 |
| SSSBorTotal —- sd | 1.535299 |
| SSSThrilTotal —- mean | 7.333333 |
| SSSThrilTotal —- sd | 2.556039 |
| SSSExpTotal —- mean | 4.619048 |
| SSSExpTotal —- sd | 2.246691 |
ANS Means
As mentioned in the manuscript, some individuals SC that exceeded the maximum threshold of 9.99 of the NeuLog instrument. Therefore, there is sample number variation between HR, SC. Additionally, there are two tasks present which subdivided the sample further.
# Full
## Baseline
stat.desc(FSFHRT1$HRbaseline) nbr.val nbr.null nbr.na min max range
92.0000000 0.0000000 0.0000000 44.3722222 117.0944444 72.7222222
sum median mean SE.mean CI.mean.0.95 var
6690.1944444 69.6916667 72.7195048 1.4816072 2.9430307 201.9547053
std.dev coef.var
14.2110769 0.1954232 stat.desc(FSFSCT1$SCbaseline) nbr.val nbr.null nbr.na min max range
89.0000000 0.0000000 0.0000000 0.1524711 4.8621228 4.7096517
sum median mean SE.mean CI.mean.0.95 var
145.8253472 1.3081056 1.6384870 0.1246785 0.2477724 1.3834813
std.dev coef.var
1.1762148 0.7178664 ## Social Stressor
stat.desc(SSFHRT1$SSHRCombAUCi) nbr.val nbr.null nbr.na min max
4.300000e+01 0.000000e+00 0.000000e+00 -2.201750e+04 8.673500e+03
range sum median mean SE.mean
3.069100e+04 -7.197100e+04 -1.750000e+02 -1.673744e+03 9.337483e+02
CI.mean.0.95 var std.dev coef.var
1.884380e+03 3.749109e+07 6.122997e+03 -3.658263e+00 stat.desc(SSFSCT1$SSSCCombAUCi) nbr.val nbr.null nbr.na min max
41.0000000 0.0000000 0.0000000 -12.2550500 1136.6175000
range sum median mean SE.mean
1148.8725500 14644.2313000 337.5527000 357.1763732 41.9285410
CI.mean.0.95 var std.dev coef.var
84.7407423 72078.1044496 268.4736569 0.7516557 ## Countdown
stat.desc(CDFHRT1$CDHRCombAuciSigaled) nbr.val nbr.null nbr.na min max
49.000000 0.000000 0.000000 -1785.500000 1071.000000
range sum median mean SE.mean
2856.500000 -14014.000000 -264.500000 -286.000000 72.785526
CI.mean.0.95 var std.dev coef.var
146.345108 259588.906250 509.498681 -1.781464 stat.desc(CDFHRT1$CDHRCombAuciUnSigaled) nbr.val nbr.null nbr.na min max
49.000000 0.000000 0.000000 -1374.000000 1595.000000
range sum median mean SE.mean
2969.000000 -8893.500000 -136.000000 -181.500000 69.072386
CI.mean.0.95 var std.dev coef.var
138.879340 233778.729167 483.506700 -2.663949 stat.desc(CDFSCT1$CDSCCombAuciSigaled) nbr.val nbr.null nbr.na min max range
48.000000 0.000000 0.000000 -23.867700 255.289800 279.157500
sum median mean SE.mean CI.mean.0.95 var
1118.414600 8.357850 23.300304 6.590098 13.257567 2084.610745
std.dev coef.var
45.657538 1.959525 stat.desc(CDFSCT1$CDSCCombAuciUnSigaled) nbr.val nbr.null nbr.na min max range
48.0000000 0.0000000 0.0000000 -72.7636500 124.8127000 197.5763500
sum median mean SE.mean CI.mean.0.95 var
25.9418500 -0.5092500 0.5404552 3.9953236 8.0375544 766.2053218
std.dev coef.var
27.6804140 51.2168513 # Male
## Baseline
stat.desc(MHRbT1$HRbaseline) nbr.val nbr.null nbr.na min max range
21.0000000 0.0000000 0.0000000 44.3722222 98.5500000 54.1777778
sum median mean SE.mean CI.mean.0.95 var
1399.8722222 66.8111111 66.6605820 2.8717408 5.9903464 173.1848022
std.dev coef.var
13.1599697 0.1974176 stat.desc(MSCbT1$SCbaseline) nbr.val nbr.null nbr.na min max range
20.0000000 0.0000000 0.0000000 0.3249450 4.8621228 4.5371778
sum median mean SE.mean CI.mean.0.95 var
39.1885278 1.5854028 1.9594264 0.2768969 0.5795520 1.5334383
std.dev coef.var
1.2383208 0.6319813 ## Social Stressor
stat.desc(MSSHRT1$SSHRCombAUCi) nbr.val nbr.null nbr.na min max
1.000000e+01 0.000000e+00 0.000000e+00 -2.201750e+04 3.197000e+03
range sum median mean SE.mean
2.521450e+04 -3.635150e+04 -2.745000e+02 -3.635150e+03 2.788795e+03
CI.mean.0.95 var std.dev coef.var
6.308693e+03 7.777378e+07 8.818944e+03 -2.426019e+00 stat.desc(MSSSCT1$SSSCCombAUCi) nbr.val nbr.null nbr.na min max
9.000000e+00 0.000000e+00 0.000000e+00 -1.225505e+01 1.136617e+03
range sum median mean SE.mean
1.148873e+03 3.096547e+03 3.375527e+02 3.440607e+02 1.105869e+02
CI.mean.0.95 var std.dev coef.var
2.550139e+02 1.100652e+05 3.317608e+02 9.642506e-01 ## Countdown
stat.desc(MCDHRT1$CDHRCombAuciSigaled) nbr.val nbr.null nbr.na min max
1.100000e+01 0.000000e+00 0.000000e+00 -1.785500e+03 7.050000e+01
range sum median mean SE.mean
1.856000e+03 -5.791500e+03 -3.705000e+02 -5.265000e+02 1.524246e+02
CI.mean.0.95 var std.dev coef.var
3.396232e+02 2.555659e+05 5.055352e+02 -9.601808e-01 stat.desc(MCDHRT1$CDHRCombAuciUnSigaled) nbr.val nbr.null nbr.na min max
11.000000 0.000000 0.000000 -702.000000 399.500000
range sum median mean SE.mean
1101.500000 -717.000000 -38.500000 -65.181818 111.047764
CI.mean.0.95 var std.dev coef.var
247.429836 135647.663636 368.303765 -5.650406 stat.desc(MCDSCT1$CDSCCombAuciSigaled) nbr.val nbr.null nbr.na min max range
11.000000 0.000000 0.000000 -23.867700 99.633050 123.500750
sum median mean SE.mean CI.mean.0.95 var
152.752550 7.089650 13.886595 9.735192 21.691358 1042.513495
std.dev coef.var
32.287978 2.325118 stat.desc(MCDSCT1$CDSCCombAuciUnSigaled) nbr.val nbr.null nbr.na min max range
11.000000 0.000000 0.000000 -72.763650 29.732350 102.496000
sum median mean SE.mean CI.mean.0.95 var
-21.784750 -0.239500 -1.980432 8.857457 19.735643 862.999912
std.dev coef.var
29.376860 -14.833563 # Female
## Baseline
stat.desc(FemaleHRbaseT1$HRbaseline) nbr.val nbr.null nbr.na min max range
71.0000000 0.0000000 0.0000000 48.9166667 117.0944444 68.1777778
sum median mean SE.mean CI.mean.0.95 var
5290.3222222 72.5888889 74.5115806 1.6732745 3.3372407 198.7891664
std.dev coef.var
14.0992612 0.1892224 stat.desc(FemaleSCbaseT1$SCbaseline) nbr.val nbr.null nbr.na min max range
69.0000000 0.0000000 0.0000000 0.1524711 4.8173183 4.6648472
sum median mean SE.mean CI.mean.0.95 var
106.6368194 1.1330900 1.5454612 0.1384621 0.2762968 1.3228510
std.dev coef.var
1.1501526 0.7442132 ## Social Stressor
stat.desc(FemaleSSHRT1$SSHRCombAUCi) nbr.val nbr.null nbr.na min max
3.300000e+01 0.000000e+00 0.000000e+00 -1.731050e+04 8.673500e+03
range sum median mean SE.mean
2.598400e+04 -3.561950e+04 -8.300000e+01 -1.079379e+03 8.836331e+02
CI.mean.0.95 var std.dev coef.var
1.799902e+03 2.576665e+07 5.076086e+03 -4.702785e+00 stat.desc(FemaleSCSST1$SSSCCombAUCi) nbr.val nbr.null nbr.na min max
32.0000000 0.0000000 0.0000000 -0.9556500 1037.5249000
range sum median mean SE.mean
1038.4805500 11547.6845500 336.5123000 360.8651422 44.9082706
CI.mean.0.95 var std.dev coef.var
91.5910218 64536.0886273 254.0395415 0.7039736 ## Countdown
stat.desc(FemaleHRCDT1$CDHRCombAuciSigaled) nbr.val nbr.null nbr.na min max
38.000000 0.000000 0.000000 -1345.000000 1071.000000
range sum median mean SE.mean
2416.000000 -8222.500000 -250.250000 -216.381579 80.380455
CI.mean.0.95 var std.dev coef.var
162.866273 245518.668030 495.498404 -2.289929 stat.desc(FemaleHRCDT1$CDHRCombAuciUnSigaled) nbr.val nbr.null nbr.na min max
38.000000 0.000000 0.000000 -1374.000000 1595.000000
range sum median mean SE.mean
2969.000000 -8176.500000 -206.500000 -215.171053 82.944527
CI.mean.0.95 var std.dev coef.var
168.061575 261432.192923 511.304403 -2.376269 stat.desc(FemaleSCCDT1$CDSCCombAuciSigaled) nbr.val nbr.null nbr.na min max range
37.000000 0.000000 0.000000 -15.210900 255.289800 270.500700
sum median mean SE.mean CI.mean.0.95 var
965.662050 12.799650 26.098974 8.048603 16.323323 2396.860237
std.dev coef.var
48.957739 1.875849 stat.desc(FemaleSCCDT1$CDSCCombAuciUnSigaled) nbr.val nbr.null nbr.na min max range
37.000000 0.000000 0.000000 -39.534550 124.812700 164.347250
sum median mean SE.mean CI.mean.0.95 var
47.726600 -0.691200 1.289908 4.526446 9.180058 758.082369
std.dev coef.var
27.533296 21.345161 t-tests
ANS
# baseline
ind.t.test1<- t.test(HRbaseline ~ Gender, data = FSFHRT1)
ind.t.test1
Welch Two Sample t-test
data: HRbaseline by Gender
t = 2.3622, df = 34.741, p-value = 0.0239
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
1.101805 14.600192
sample estimates:
mean in group Female mean in group Male
74.51158 66.66058 ind.t.test1<- t.test(SCbaseline ~ Gender, data = FSFSCT1)
ind.t.test1
Welch Two Sample t-test
data: SCbaseline by Gender
t = -1.3372, df = 29.18, p-value = 0.1915
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-1.0469706 0.2190401
sample estimates:
mean in group Female mean in group Male
1.545461 1.959426 #SS
ind.t.test1<- t.test(SSHRCombAUCi ~ Gender, data = SSFHRT1)
ind.t.test1
Welch Two Sample t-test
data: SSHRCombAUCi by Gender
t = 0.87364, df = 10.867, p-value = 0.4012
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-3892.701 9004.243
sample estimates:
mean in group Female mean in group Male
-1079.379 -3635.150 ind.t.test1<- t.test(SSSCCombAUCi ~ Gender, data = SSFSCT1)
ind.t.test1
Welch Two Sample t-test
data: SSSCCombAUCi by Gender
t = 0.14079, df = 10.78, p-value = 0.8906
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-246.5540 280.1628
sample estimates:
mean in group Female mean in group Male
360.8651 344.0607 # CD
ind.t.test1<- t.test(CDHRCombAuciSigaled ~ Gender, data = CDFHRT1)
ind.t.test1
Welch Two Sample t-test
data: CDHRCombAuciSigaled by Gender
t = 1.7997, df = 16.001, p-value = 0.0908
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-55.18278 675.41962
sample estimates:
mean in group Female mean in group Male
-216.3816 -526.5000 ind.t.test1<- t.test(CDHRCombAuciUnSigaled ~ Gender, data = CDFHRT1)
ind.t.test1
Welch Two Sample t-test
data: CDHRCombAuciUnSigaled by Gender
t = -1.0821, df = 22.387, p-value = 0.2907
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-437.1508 137.1723
sample estimates:
mean in group Female mean in group Male
-215.17105 -65.18182 ind.t.test1<- t.test(CDSCCombAuciSigaled ~ Gender, data = CDFSCT1)
ind.t.test1
Welch Two Sample t-test
data: CDSCCombAuciSigaled by Gender
t = 0.96682, df = 25.087, p-value = 0.3429
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-13.79805 38.22281
sample estimates:
mean in group Female mean in group Male
26.09897 13.88660 ind.t.test1<- t.test(CDSCCombAuciUnSigaled ~ Gender, data = CDFSCT1)
ind.t.test1
Welch Two Sample t-test
data: CDSCCombAuciUnSigaled by Gender
t = 0.32878, df = 15.609, p-value = 0.7467
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-17.85937 24.40005
sample estimates:
mean in group Female mean in group Male
1.289908 -1.980432 Survey
# SRP
ind.t.test1<- t.test(SRPTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SRPTotalScore by Gender
t = -2.424, df = 46.285, p-value = 0.01932
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-20.594560 -1.909799
sample estimates:
mean in group Female mean in group Male
138.9859 150.2381 ind.t.test1<- t.test(SRPIPMTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SRPIPMTotal by Gender
t = -1.6063, df = 49.122, p-value = 0.1146
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-5.5029328 0.6135968
sample estimates:
mean in group Female mean in group Male
37.12676 39.57143 ind.t.test1<- t.test(SRPCATotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SRPCATotal by Gender
t = -3.9353, df = 30.13, p-value = 0.0004535
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-10.978471 -3.477599
sample estimates:
mean in group Female mean in group Male
35.29577 42.52381 ind.t.test1<- t.test(SRPELSTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SRPELSTotal by Gender
t = -0.57104, df = 43.211, p-value = 0.5709
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-4.914022 2.745008
sample estimates:
mean in group Female mean in group Male
41.91549 43.00000 ind.t.test1<- t.test(SRPASBTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SRPASBTotal by Gender
t = -0.27844, df = 31.625, p-value = 0.7825
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-4.117561 3.127621
sample estimates:
mean in group Female mean in group Male
24.64789 25.14286 # ICU
ind.t.test1<- t.test(ICUTotScore ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: ICUTotScore by Gender
t = -3.07, df = 40.837, p-value = 0.003796
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-7.665736 -1.581749
sample estimates:
mean in group Female mean in group Male
41.66197 46.28571 ind.t.test1<- t.test(ICUCalTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: ICUCalTotalScore by Gender
t = -2.3603, df = 27.459, p-value = 0.02561
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-2.630642 -0.184918
sample estimates:
mean in group Female mean in group Male
15.21127 16.61905 ind.t.test1<- t.test(ICUUncareTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: ICUUncareTotalScore by Gender
t = -1.4295, df = 46.976, p-value = 0.1595
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-2.5816516 0.4367824
sample estimates:
mean in group Female mean in group Male
14.07042 15.14286 ind.t.test1<- t.test(ICUUnemoTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: ICUUnemoTotal by Gender
t = -2.2486, df = 32.625, p-value = 0.03142
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-4.0838237 -0.2032319
sample estimates:
mean in group Female mean in group Male
12.38028 14.52381 # LSRP
ind.t.test1<- t.test(LevTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: LevTotalScore by Gender
t = -2.0005, df = 41.647, p-value = 0.05199
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-6.54718941 0.02941611
sample estimates:
mean in group Female mean in group Male
45.78873 49.04762 ind.t.test1<- t.test(LevPrimTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: LevPrimTotalScore by Gender
t = -2.1956, df = 33.799, p-value = 0.03509
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-5.2801499 -0.2034181
sample estimates:
mean in group Female mean in group Male
27.59155 30.33333 ind.t.test1<- t.test(LevSecTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: LevSecTotalScore by Gender
t = -0.70821, df = 47.259, p-value = 0.4823
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-1.9857677 0.9515625
sample estimates:
mean in group Female mean in group Male
18.19718 18.71429 # SSS
ind.t.test1<- t.test(SSSTotalScore ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SSSTotalScore by Gender
t = -1.2108, df = 38.168, p-value = 0.2334
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-4.037142 1.015009
sample estimates:
mean in group Female mean in group Male
16.77465 18.28571 ind.t.test1<- t.test(SSSDISTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SSSDISTotal by Gender
t = 0.16959, df = 35.41, p-value = 0.8663
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-1.044363 1.234840
sample estimates:
mean in group Female mean in group Male
4.000000 3.904762 ind.t.test1<- t.test(SSSBorTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SSSBorTotal by Gender
t = -1.1681, df = 35.762, p-value = 0.2505
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-1.2498993 0.3364184
sample estimates:
mean in group Female mean in group Male
1.971831 2.428571 ind.t.test1<- t.test(SSSThrilTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SSSThrilTotal by Gender
t = -2.4666, df = 36.485, p-value = 0.01846
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-2.9422934 -0.2877535
sample estimates:
mean in group Female mean in group Male
5.718310 7.333333 ind.t.test1<- t.test(SSSExpTotal ~ Gender, data = FSFSurveyT1)
ind.t.test1
Welch Two Sample t-test
data: SSSExpTotal by Gender
t = 0.87885, df = 27.022, p-value = 0.3872
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-0.6211987 1.5521175
sample estimates:
mean in group Female mean in group Male
5.084507 4.619048 Distributions of DVs
# Histogram function
histo <- function(df, var, title = "Histogram", xlab = "DV", ylab = "Frequency", col = "honeydew", border = "black", bins = 5){
df |>
ggplot(aes(x = {{var}})) +
geom_histogram(binwidth = bins, fill = col, color = border) +
labs(title = title, x = xlab, y = ylab)
}SRP Full
Normal = SRPTot, SRPIPMTotal, SRPCATotal, SRPELSTotal
Non-Normal = SRPASBTotal
# SRPTot
FSFSurveyT1 |>
histo(SRPTotalScore)
qqnorm(FSFSurveyT1$SRPTotalScore)
qqline(FSFSurveyT1$SRPTotalScore)
shapiro.test(FSFSurveyT1$SRPTotalScore)
Shapiro-Wilk normality test
data: FSFSurveyT1$SRPTotalScore
W = 0.97854, p-value = 0.1335# SRP IPM
FSFSurveyT1 |>
histo(SRPIPMTotal)
qqnorm(FSFSurveyT1$SRPIPMTotal)
qqline(FSFSurveyT1$SRPIPMTotal)
shapiro.test(FSFSurveyT1$SRPIPMTotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$SRPIPMTotal
W = 0.99234, p-value = 0.8779# SRPCATotal
FSFSurveyT1 |>
histo(SRPCATotal)
qqnorm(FSFSurveyT1$SRPCATotal)
qqline(FSFSurveyT1$SRPCATotal)
shapiro.test(FSFSurveyT1$SRPCATotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$SRPCATotal
W = 0.98428, p-value = 0.3365# SRPELSTotal
FSFSurveyT1 |>
histo(SRPELSTotal)
qqnorm(FSFSurveyT1$SRPELSTotal)
qqline(FSFSurveyT1$SRPELSTotal)
shapiro.test(FSFSurveyT1$SRPELSTotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$SRPELSTotal
W = 0.97861, p-value = 0.1352# SRPASBTotal
FSFSurveyT1 |>
histo(SRPASBTotal)
qqnorm(FSFSurveyT1$SRPASBTotal)
qqline(FSFSurveyT1$SRPASBTotal)
shapiro.test(FSFSurveyT1$SRPASBTotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$SRPASBTotal
W = 0.93314, p-value = 0.0001476ICU Full
Non-Normal = ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore, ICUUnemoTotal
# ICUtotal
FSFSurveyT1 |>
histo(ICUTotScore)
qqnorm(FSFSurveyT1$ICUTotScore)
qqline(FSFSurveyT1$ICUTotScore)
shapiro.test(FSFSurveyT1$ICUTotScore)
Shapiro-Wilk normality test
data: FSFSurveyT1$ICUTotScore
W = 0.96482, p-value = 0.01386# ICU cal
FSFSurveyT1 |>
histo(ICUCalTotalScore)
qqnorm(FSFSurveyT1$ICUCalTotalScore)
qqline(FSFSurveyT1$ICUCalTotalScore)
shapiro.test(FSFSurveyT1$ICUCalTotalScore)
Shapiro-Wilk normality test
data: FSFSurveyT1$ICUCalTotalScore
W = 0.95915, p-value = 0.005679# ICUUncare
FSFSurveyT1 |>
histo(ICUUncareTotalScore)
qqnorm(FSFSurveyT1$ICUUncareTotalScore)
qqline(FSFSurveyT1$ICUUncareTotalScore)
shapiro.test(FSFSurveyT1$ICUUncareTotalScore)
Shapiro-Wilk normality test
data: FSFSurveyT1$ICUUncareTotalScore
W = 0.96571, p-value = 0.01598# Unemo
FSFSurveyT1 |>
histo(ICUUnemoTotal)
qqnorm(FSFSurveyT1$ICUUnemoTotal)
qqline(FSFSurveyT1$ICUUnemoTotal)
shapiro.test(FSFSurveyT1$ICUUnemoTotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$ICUUnemoTotal
W = 0.97151, p-value = 0.0413Lev full
Normal = LevTotalScore, LevSecTotalScore
Non-Normal = LevPrimTotalScore
# Leve tot
FSFSurveyT1 |>
histo(LevTotalScore)
qqnorm(FSFSurveyT1$LevTotalScore)
qqline(FSFSurveyT1$LevTotalScore)
shapiro.test(FSFSurveyT1$LevTotalScore)
Shapiro-Wilk normality test
data: FSFSurveyT1$LevTotalScore
W = 0.97816, p-value = 0.1254# lev prim
FSFSurveyT1 |>
histo(LevPrimTotalScore)
qqnorm(FSFSurveyT1$LevPrimTotalScore)
qqline(FSFSurveyT1$LevPrimTotalScore)
shapiro.test(FSFSurveyT1$LevPrimTotalScore)
Shapiro-Wilk normality test
data: FSFSurveyT1$LevPrimTotalScore
W = 0.96646, p-value = 0.01804# lev sec
FSFSurveyT1 |>
histo(LevSecTotalScore)
qqnorm(FSFSurveyT1$LevSecTotalScore)
qqline(FSFSurveyT1$LevSecTotalScore)
shapiro.test(FSFSurveyT1$LevSecTotalScore)
Shapiro-Wilk normality test
data: FSFSurveyT1$LevSecTotalScore
W = 0.97963, p-value = 0.1598SSS Full
Normal = SSSTotalScore
Non-Normal = SSSDISTotal, SSSBorTotal, SSSThrilTotal, SSSExpTotal
# SSS total
FSFSurveyT1 |>
histo(SSSTotalScore)
qqnorm(FSFSurveyT1$SSSTotalScore)
qqline(FSFSurveyT1$SSSTotalScore)
shapiro.test(FSFSurveyT1$SSSTotalScore)
Shapiro-Wilk normality test
data: FSFSurveyT1$SSSTotalScore
W = 0.97996, p-value = 0.1689# SSS dis
FSFSurveyT1 |>
histo(SSSDISTotal)
qqnorm(FSFSurveyT1$SSSDISTotal)
qqline(FSFSurveyT1$SSSDISTotal)
shapiro.test(FSFSurveyT1$SSSDISTotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$SSSDISTotal
W = 0.95666, p-value = 0.003886# SSSBorTotal
FSFSurveyT1 |>
histo(SSSBorTotal)
qqnorm(FSFSurveyT1$SSSBorTotal)
qqline(FSFSurveyT1$SSSBorTotal)
shapiro.test(FSFSurveyT1$SSSBorTotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$SSSBorTotal
W = 0.89337, p-value = 1.66e-06# SSSThrilTotal
FSFSurveyT1 |>
histo(SSSThrilTotal)
qqnorm(FSFSurveyT1$SSSThrilTotal)
qqline(FSFSurveyT1$SSSThrilTotal)
shapiro.test(FSFSurveyT1$SSSThrilTotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$SSSThrilTotal
W = 0.92751, p-value = 7.295e-05# SSS exp
FSFSurveyT1 |>
histo(SSSExpTotal)
qqnorm(FSFSurveyT1$SSSExpTotal)
qqline(FSFSurveyT1$SSSExpTotal)
shapiro.test(FSFSurveyT1$SSSExpTotal)
Shapiro-Wilk normality test
data: FSFSurveyT1$SSSExpTotal
W = 0.96231, p-value = 0.009297SRP Female
Normal = SRPTotalScore, SRPIPMTotal, SRPCATotal, SRPELSTotal
Non-Normal = SRPASBTotal
# SRPTot
FemaleDisCheck |>
histo(SRPTotalScore)
qqnorm(FemaleDisCheck$SRPTotalScore)
qqline(FemaleDisCheck$SRPTotalScore)
shapiro.test(FemaleDisCheck$SRPTotalScore)
Shapiro-Wilk normality test
data: FemaleDisCheck$SRPTotalScore
W = 0.98271, p-value = 0.4361# SRP IPM
FemaleDisCheck |>
histo(SRPIPMTotal)
qqnorm(FemaleDisCheck$SRPIPMTotal)
qqline(FemaleDisCheck$SRPIPMTotal)
shapiro.test(FemaleDisCheck$SRPIPMTotal)
Shapiro-Wilk normality test
data: FemaleDisCheck$SRPIPMTotal
W = 0.99078, p-value = 0.8863# SRPCATotal
FemaleDisCheck |>
histo(SRPCATotal)
qqnorm(FemaleDisCheck$SRPCATotal)
qqline(FemaleDisCheck$SRPCATotal)
shapiro.test(FemaleDisCheck$SRPCATotal)
Shapiro-Wilk normality test
data: FemaleDisCheck$SRPCATotal
W = 0.98397, p-value = 0.5015# SRPELSTotal
FemaleDisCheck |>
histo(SRPELSTotal)
qqnorm(FemaleDisCheck$SRPELSTotal)
qqline(FemaleDisCheck$SRPELSTotal)
shapiro.test(FemaleDisCheck$SRPELSTotal)
Shapiro-Wilk normality test
data: FemaleDisCheck$SRPELSTotal
W = 0.97613, p-value = 0.1943# SRPASBTotal
FemaleDisCheck |>
histo(SRPASBTotal)
qqnorm(FemaleDisCheck$SRPASBTotal)
qqline(FemaleDisCheck$SRPASBTotal)
shapiro.test(FemaleDisCheck$SRPASBTotal)
Shapiro-Wilk normality test
data: FemaleDisCheck$SRPASBTotal
W = 0.93521, p-value = 0.0012ICU Female
Normal = ICUUnemoTotal
Non-Normal = ICUTotScore, ICUCalTotalScore, ICUUncareTotalScore,
# ICUtotal
FemaleDisCheck |>
histo(ICUTotScore)
qqnorm(FemaleDisCheck$ICUTotScore)
qqline(FemaleDisCheck$ICUTotScore)
shapiro.test(FemaleDisCheck$ICUTotScore)
Shapiro-Wilk normality test
data: FemaleDisCheck$ICUTotScore
W = 0.96252, p-value = 0.03254# ICU cal
FemaleDisCheck |>
histo(ICUCalTotalScore)
qqnorm(FemaleDisCheck$ICUCalTotalScore)
qqline(FemaleDisCheck$ICUCalTotalScore)
shapiro.test(FemaleDisCheck$ICUCalTotalScore)
Shapiro-Wilk normality test
data: FemaleDisCheck$ICUCalTotalScore
W = 0.94579, p-value = 0.004055# ICUUncare
FemaleDisCheck |>
histo(ICUUncareTotalScore)
qqnorm(FemaleDisCheck$ICUUncareTotalScore)
qqline(FemaleDisCheck$ICUUncareTotalScore)
shapiro.test(FemaleDisCheck$ICUUncareTotalScore)
Shapiro-Wilk normality test
data: FemaleDisCheck$ICUUncareTotalScore
W = 0.94513, p-value = 0.003748# Unemo
FemaleDisCheck |>
histo(ICUUnemoTotal)
qqnorm(FemaleDisCheck$ICUUnemoTotal)
qqline(FemaleDisCheck$ICUUnemoTotal)
shapiro.test(FemaleDisCheck$ICUUnemoTotal)
Shapiro-Wilk normality test
data: FemaleDisCheck$ICUUnemoTotal
W = 0.97343, p-value = 0.1366Lev Female
Normal = LevTotalScore, LevSecTotalScore
Non-Normal = LevPrimTotalScore
# Leve tot
FemaleDisCheck |>
histo(LevTotalScore)
qqnorm(FemaleDisCheck$LevTotalScore)
qqline(FemaleDisCheck$LevTotalScore)
shapiro.test(FemaleDisCheck$LevTotalScore)
Shapiro-Wilk normality test
data: FemaleDisCheck$LevTotalScore
W = 0.97601, p-value = 0.1913# lev prim
FemaleDisCheck |>
histo(LevPrimTotalScore)
qqnorm(FemaleDisCheck$LevPrimTotalScore)
qqline(FemaleDisCheck$LevPrimTotalScore)
shapiro.test(FemaleDisCheck$LevPrimTotalScore)
Shapiro-Wilk normality test
data: FemaleDisCheck$LevPrimTotalScore
W = 0.95639, p-value = 0.01485# lev sec
FemaleDisCheck |>
histo(LevSecTotalScore)
qqnorm(FemaleDisCheck$LevSecTotalScore)
qqline(FemaleDisCheck$LevSecTotalScore)
shapiro.test(FemaleDisCheck$LevSecTotalScore)
Shapiro-Wilk normality test
data: FemaleDisCheck$LevSecTotalScore
W = 0.97734, p-value = 0.227SSS Female
Normal = SSSTotalScore
Non-Normal = SSSDISTotal, SSSBorTotal, SSSThrilTotal, SSSExpTotal
# SSS total
FemaleDisCheck |>
histo(SSSTotalScore)
qqnorm(FemaleDisCheck$SSSTotalScore)
qqline(FemaleDisCheck$SSSTotalScore)
shapiro.test(FemaleDisCheck$SSSTotalScore)
Shapiro-Wilk normality test
data: FemaleDisCheck$SSSTotalScore
W = 0.98541, p-value = 0.5828# SSS dis
FemaleDisCheck |>
histo(SSSDISTotal)
qqnorm(FemaleDisCheck$SSSDISTotal)
qqline(FemaleDisCheck$SSSDISTotal)
shapiro.test(FemaleDisCheck$SSSDISTotal)
Shapiro-Wilk normality test
data: FemaleDisCheck$SSSDISTotal
W = 0.95206, p-value = 0.008654# SSSBorTotal
FemaleDisCheck |>
histo(SSSBorTotal)
qqnorm(FemaleDisCheck$SSSBorTotal)
qqline(FemaleDisCheck$SSSBorTotal)
shapiro.test(FemaleDisCheck$SSSBorTotal)
Shapiro-Wilk normality test
data: FemaleDisCheck$SSSBorTotal
W = 0.88064, p-value = 6.346e-06# SSSThrilTotal
FemaleDisCheck |>
histo(SSSThrilTotal)
qqnorm(FemaleDisCheck$SSSThrilTotal)
qqline(FemaleDisCheck$SSSThrilTotal)
shapiro.test(FemaleDisCheck$SSSThrilTotal)
Shapiro-Wilk normality test
data: FemaleDisCheck$SSSThrilTotal
W = 0.94616, p-value = 0.004233# SSS exp
FemaleDisCheck |>
histo(SSSExpTotal)
qqnorm(FemaleDisCheck$SSSExpTotal)
qqline(FemaleDisCheck$SSSExpTotal)
shapiro.test(FemaleDisCheck$SSSExpTotal)
Shapiro-Wilk normality test
data: FemaleDisCheck$SSSExpTotal
W = 0.96367, p-value = 0.03783Table 2 (Partial Correlations)
Full
HR baseline
#SRP
pcor.test(FSFHRT1$SRPTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.02601355 0.8087933 -0.2427204 92 3 pearsonpcor.test(FSFHRT1$SRPIPMTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.004966072 0.9631606 0.04632101 92 3 pearsonpcor.test(FSFHRT1$SRPCATotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.06169229 0.5657486 0.5765256 92 3 pearsonpcor.test(FSFHRT1$SRPELSTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.06158436 0.5664299 -0.5755131 92 3 pearsonpcor.test(FSFHRT1$SRPASBTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.03117451 0.7718066 -0.2909178 92 3 spearman# ICU
pcor.test(FSFHRT1$ICUTotScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.08304044 0.439129 0.7772341 92 3 spearmanpcor.test(FSFHRT1$ICUCalTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.09684776 0.3665955 0.9076023 92 3 spearmanpcor.test(FSFHRT1$ICUUncareTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.05813552 0.5884013 0.5431707 92 3 spearmanpcor.test(FSFHRT1$ICUUnemoTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.04174293 0.6977168 0.3896918 92 3 spearman# Lev
pcor.test(FSFHRT1$LevTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.03213252 0.7649938 0.2998671 92 3 pearsonpcor.test(FSFHRT1$LevPrimTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.00209549 0.9844507 -0.01954547 92 3 spearmanpcor.test(FSFHRT1$LevSecTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.02490207 0.8168172 -0.2323431 92 3 pearson# SSS
pcor.test(FSFHRT1$SSSTotalScore, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.08447431 0.43124 -0.7907503 92 3 pearsonpcor.test(FSFHRT1$SSSDISTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.05866318 0.5850148 0.5481177 92 3 spearmanpcor.test(FSFHRT1$SSSBorTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.07000974 0.5144435 -0.6546136 92 3 spearmanpcor.test(FSFHRT1$SSSThrilTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1334697 0.21242 -1.256162 92 3 spearmanpcor.test(FSFHRT1$SSSExpTotal, FSFHRT1$HRbaseline, list(FSFHRT1$Female, FSFHRT1$White, FSFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.06475256 0.5465938 0.6052419 92 3 spearmanSC baseline
#SRP
pcor.test(FSFSCT1$SRPTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age),method = "pearson") estimate p.value statistic n gp Method
1 -0.1724679 0.1123041 -1.604741 89 3 pearsonpcor.test(FSFSCT1$SRPIPMTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.00512591 0.9626404 0.04698036 89 3 pearsonpcor.test(FSFSCT1$SRPCATotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.137153 0.207939 -1.26902 89 3 pearsonpcor.test(FSFSCT1$SRPELSTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.3137552 0.003263775 -3.028544 89 3 pearsonpcor.test(FSFSCT1$SRPASBTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.002064273 0.9849503 -0.01891942 89 3 spearman# ICU
pcor.test(FSFSCT1$ICUTotScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.002713572 0.9802173 -0.02487039 89 3 spearmanpcor.test(FSFSCT1$ICUCalTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.2093414 0.05305946 -1.962121 89 3 spearmanpcor.test(FSFSCT1$ICUUncareTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.0009128471 0.9933445 -0.008366385 89 3 spearmanpcor.test(FSFSCT1$ICUUnemoTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1374803 0.2068455 1.272107 89 3 spearman# Lev
pcor.test(FSFSCT1$LevTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1052439 0.3348497 -0.9699629 89 3 pearsonpcor.test(FSFSCT1$LevPrimTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.08259219 0.4496411 -0.759565 89 3 spearmanpcor.test(FSFSCT1$LevSecTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1509196 0.1654266 -1.399228 89 3 pearson# SSS
pcor.test(FSFSCT1$SSSTotalScore, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.2320159 0.03158946 -2.186116 89 3 pearsonpcor.test(FSFSCT1$SSSDISTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.09802261 0.3692456 -0.9027394 89 3 spearmanpcor.test(FSFSCT1$SSSBorTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1049607 0.3361592 -0.9673236 89 3 spearmanpcor.test(FSFSCT1$SSSThrilTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.06113907 0.5760202 -0.5613991 89 3 spearmanpcor.test(FSFSCT1$SSSExpTotal, FSFSCT1$SCbaseline, list(FSFSCT1$Female, FSFSCT1$White, FSFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1450858 0.1825822 -1.343954 89 3 spearmanCountdown
## HR Signaled
#SRP
pcor.test(CDFHRT1$SRPTotalScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age),method = "pearson") estimate p.value statistic n gp Method
1 -0.401147 0.005728543 -2.904878 49 3 pearsonpcor.test(CDFHRT1$SRPIPMTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.3199755 0.03017364 -2.240257 49 3 pearsonpcor.test(CDFHRT1$SRPCATotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.2087954 0.1637559 -1.416206 49 3 pearsonpcor.test(CDFHRT1$SRPELSTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.3476516 0.01791516 -2.459472 49 3 pearsonpcor.test(CDFHRT1$SRPASBTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.3333628 0.02357762 -2.345441 49 3 spearman# ICU
pcor.test(CDFHRT1$ICUTotScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1020763 0.4996575 -0.6806529 49 3 spearmanpcor.test(CDFHRT1$ICUCalTotalScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.2154974 0.1503453 -1.463842 49 3 spearmanpcor.test(CDFHRT1$ICUUncareTotalScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.098444 0.5151184 -0.656191 49 3 spearmanpcor.test(CDFHRT1$ICUUnemoTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.04755639 0.7536394 0.3158107 49 3 spearman# Lev
pcor.test(CDFHRT1$LevTotalScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1337553 0.3755109 -0.8952767 49 3 pearsonpcor.test(CDFHRT1$LevPrimTotalScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.05625032 0.7104117 -0.3737141 49 3 spearmanpcor.test(CDFHRT1$LevSecTotalScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1708016 0.2564105 -1.149867 49 3 pearson# SSS
pcor.test(CDFHRT1$SSSTotalScore, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.333356 0.0235806 -2.345387 49 3 pearsonpcor.test(CDFHRT1$SSSDISTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.3057151 0.03881397 -2.129856 49 3 spearmanpcor.test(CDFHRT1$SSSBorTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1232979 0.4142971 -0.8241545 49 3 spearmanpcor.test(CDFHRT1$SSSThrilTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1461885 0.332329 -0.9802358 49 3 spearmanpcor.test(CDFHRT1$SSSExpTotal, CDFHRT1$CDHRCombAuciSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.2081389 0.1651149 -1.411551 49 3 spearman## HR Unsignaled
#SRP
pcor.test(CDFHRT1$SRPTotalScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age),method = "pearson") estimate p.value statistic n gp Method
1 0.1740436 0.2473582 1.172367 49 3 pearsonpcor.test(CDFHRT1$SRPIPMTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.07598876 0.615721 0.505514 49 3 pearsonpcor.test(CDFHRT1$SRPCATotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.173424 0.2490713 1.168064 49 3 pearsonpcor.test(CDFHRT1$SRPELSTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.1393526 0.3556727 0.9334684 49 3 pearsonpcor.test(CDFHRT1$SRPASBTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.07038923 0.6420447 0.4680703 49 3 spearman# ICU
pcor.test(CDFHRT1$ICUTotScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.02240489 0.8825055 0.1486545 49 3 spearmanpcor.test(CDFHRT1$ICUCalTotalScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1252307 0.4069614 0.8372775 49 3 spearmanpcor.test(CDFHRT1$ICUUncareTotalScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.002311066 0.9878383 0.01532992 49 3 spearmanpcor.test(CDFHRT1$ICUUnemoTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.04180003 0.7826863 -0.2775126 49 3 spearman# Lev
pcor.test(CDFHRT1$LevTotalScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.02899293 0.8483155 0.1923982 49 3 pearsonpcor.test(CDFHRT1$LevPrimTotalScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1406559 0.3511469 0.942374 49 3 spearmanpcor.test(CDFHRT1$LevSecTotalScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.03643697 0.8100153 0.2418561 49 3 pearson# SSS
pcor.test(CDFHRT1$SSSTotalScore, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.1722155 0.2524357 1.159675 49 3 pearsonpcor.test(CDFHRT1$SSSDISTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1685004 0.2629693 1.133918 49 3 spearmanpcor.test(CDFHRT1$SSSBorTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.2702601 0.06929107 1.861993 49 3 spearmanpcor.test(CDFHRT1$SSSThrilTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1351967 0.3703401 0.9051036 49 3 spearmanpcor.test(CDFHRT1$SSSExpTotal, CDFHRT1$CDHRCombAuciUnSigaled, list(CDFHRT1$Female, CDFHRT1$White, CDFHRT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1075471 0.4768282 -0.7175485 49 3 spearman## SC signaled
#SRP
pcor.test(CDFSCT1$SRPTotalScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age),method = "pearson") estimate p.value statistic n gp Method
1 -0.09964416 0.5148868 -0.6566786 48 3 pearsonpcor.test(CDFSCT1$SRPIPMTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.0962247 0.529485 -0.6339292 48 3 pearsonpcor.test(CDFSCT1$SRPCATotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.01947212 0.8989727 -0.1277114 48 3 pearsonpcor.test(CDFSCT1$SRPELSTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.0814333 0.5948759 -0.5357733 48 3 pearsonpcor.test(CDFSCT1$SRPASBTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.08153656 0.5944073 0.5364572 48 3 spearman# ICU
pcor.test(CDFSCT1$ICUTotScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.06143973 0.6884706 -0.4036498 48 3 spearmanpcor.test(CDFSCT1$ICUCalTotalScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1007967 0.5100122 0.664352 48 3 spearmanpcor.test(CDFSCT1$ICUUncareTotalScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.01927312 0.8999999 0.1264058 48 3 spearmanpcor.test(CDFSCT1$ICUUnemoTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1495447 0.3268519 -0.9917828 48 3 spearman# Lev
pcor.test(CDFSCT1$LevTotalScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1017953 0.505808 -0.6710018 48 3 pearsonpcor.test(CDFSCT1$LevPrimTotalScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1340788 0.3798937 0.8872245 48 3 spearmanpcor.test(CDFSCT1$LevSecTotalScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1855536 0.2223378 -1.238259 48 3 pearson# SSS
pcor.test(CDFSCT1$SSSTotalScore, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.04003091 0.7940281 0.2627108 48 3 pearsonpcor.test(CDFSCT1$SSSDISTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.164949 0.2788917 1.096665 48 3 spearmanpcor.test(CDFSCT1$SSSBorTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.3265378 0.02857926 2.265433 48 3 spearmanpcor.test(CDFSCT1$SSSThrilTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.01341004 0.9303299 0.08794342 48 3 spearmanpcor.test(CDFSCT1$SSSExpTotal, CDFSCT1$CDSCCombAuciSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1778328 0.2425193 1.185016 48 3 spearman## SC Unsignaled
#SRP
pcor.test(CDFSCT1$SRPTotalScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age),method = "pearson") estimate p.value statistic n gp Method
1 0.02509109 0.8700418 0.1645851 48 3 pearsonpcor.test(CDFSCT1$SRPIPMTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.1762964 0.2466788 1.174448 48 3 pearsonpcor.test(CDFSCT1$SRPCATotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.03363885 0.8263632 0.2207096 48 3 pearsonpcor.test(CDFSCT1$SRPELSTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.02189283 0.8864909 -0.1435953 48 3 pearsonpcor.test(CDFSCT1$SRPASBTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1704498 0.2629472 -1.134313 48 3 spearman# ICU
pcor.test(CDFSCT1$ICUTotScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1248648 0.4137834 -0.8252522 48 3 spearmanpcor.test(CDFSCT1$ICUCalTotalScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.09370631 0.5403646 -0.6171891 48 3 spearmanpcor.test(CDFSCT1$ICUUncareTotalScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.0613446 0.6889286 -0.4030225 48 3 spearmanpcor.test(CDFSCT1$ICUUnemoTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.0443013 0.772611 -0.2907886 48 3 spearman# Lev
pcor.test(CDFSCT1$LevTotalScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.08157799 0.5942194 0.5367316 48 3 pearsonpcor.test(CDFSCT1$LevPrimTotalScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.02536905 0.8686147 -0.1664095 48 3 spearmanpcor.test(CDFSCT1$LevSecTotalScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.02855391 0.8522933 0.1873169 48 3 pearson# SSS
pcor.test(CDFSCT1$SSSTotalScore, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.148852 0.3291231 0.9870845 48 3 pearsonpcor.test(CDFSCT1$SSSDISTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.2203832 0.1457446 1.481576 48 3 spearmanpcor.test(CDFSCT1$SSSBorTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.3146555 0.03527598 2.173748 48 3 spearmanpcor.test(CDFSCT1$SSSThrilTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1986524 0.190811 1.329141 48 3 spearmanpcor.test(CDFSCT1$SSSExpTotal, CDFSCT1$CDSCCombAuciUnSigaled, list(CDFSCT1$Female, CDFSCT1$White, CDFSCT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1119332 0.4641402 0.7386371 48 3 spearmanFemale Only
HR baseline
# SRP
pcor.test(FemaleHRbaseT1$SRPTot, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.007961143 0.9482352 0.06516683 71 2 pearsonpcor.test(FemaleHRbaseT1$SRPIPM, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.02836029 0.8170654 0.2322324 71 2 pearsonpcor.test(FemaleHRbaseT1$SRPCA, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.1124774 0.3574877 0.9265464 71 2 pearsonpcor.test(FemaleHRbaseT1$SRPELS, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.06303849 0.6068444 -0.5170206 71 2 pearsonpcor.test(FemaleHRbaseT1$SRPASB, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.04716191 0.7003756 0.3864669 71 2 spearman# ICU
pcor.test(FemaleHRbaseT1$ICUTot, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1767543 0.1462585 1.46994 71 2 spearmanpcor.test(FemaleHRbaseT1$ICUCal, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1486528 0.2228325 1.230447 71 2 spearmanpcor.test(FemaleHRbaseT1$ICUUncare, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1552206 0.2028269 1.286123 71 2 spearmanpcor.test(FemaleHRbaseT1$ICUUnemo, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.1406379 0.2490649 1.162727 71 2 pearson# Lev
pcor.test(FemaleHRbaseT1$LevTot, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.03947964 0.7473944 0.3234069 71 2 pearsonpcor.test(FemaleHRbaseT1$LevPrim, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.005765758 0.9624978 0.04719555 71 2 spearmanpcor.test(FemaleHRbaseT1$LevSec, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.005179169 0.9663108 -0.0423939 71 2 pearson# SSS
pcor.test(FemaleHRbaseT1$SSSTot, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.06253916 0.6097019 -0.5129091 71 2 pearsonpcor.test(FemaleHRbaseT1$SSSDIS, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.0476913 0.6971741 0.3908148 71 2 spearmanpcor.test(FemaleHRbaseT1$SSSBor, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.05905643 0.6297927 -0.4842429 71 2 spearmanpcor.test(FemaleHRbaseT1$SSSThril, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.06631459 0.5882433 -0.5440058 71 2 spearmanpcor.test(FemaleHRbaseT1$SSSExp, FemaleHRbaseT1$HRbaseline, list(FemaleHRbaseT1$White, FemaleHRbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.0529366 0.6657456 0.4339132 71 2 spearmanSC baseline
#SRP
pcor.test(FemaleSCbaseT1$SRPTot, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1870743 0.1295523 -1.535347 69 2 pearsonpcor.test(FemaleSCbaseT1$SRPIPM, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.008563325 0.9451679 -0.06904227 69 2 pearsonpcor.test(FemaleSCbaseT1$SRPCA, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1230658 0.3211205 -0.9997882 69 2 pearsonpcor.test(FemaleSCbaseT1$SRPELS, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.3316084 0.006120008 -2.83386 69 2 pearsonpcor.test(FemaleSCbaseT1$SRPASB, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.007109186 0.9544681 0.05731754 69 2 spearman# ICU
pcor.test(FemaleSCbaseT1$ICUTot, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.006168834 0.9604855 0.04973568 69 2 spearmanpcor.test(FemaleSCbaseT1$ICUCal, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1530214 0.2163625 -1.2484 69 2 spearmanpcor.test(FemaleSCbaseT1$ICUUncare, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.08918342 0.4729473 -0.7218963 69 2 spearmanpcor.test(FemaleSCbaseT1$ICUUnemo, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.1927545 0.1181076 1.583736 69 2 pearson# Lev
pcor.test(FemaleSCbaseT1$LevTot, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1800692 0.1448018 -1.475889 69 2 pearsonpcor.test(FemaleSCbaseT1$LevPrim, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1599716 0.1959669 -1.306558 69 2 spearmanpcor.test(FemaleSCbaseT1$LevSec, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.2070063 0.09280329 -1.705889 69 2 pearson# SSS
pcor.test(FemaleSCbaseT1$SSSTot, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.3143761 0.009572456 -2.669952 69 2 pearsonpcor.test(FemaleSCbaseT1$SSSDIS, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.2475152 0.04344437 -2.059618 69 2 spearmanpcor.test(FemaleSCbaseT1$SSSBor, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.05325435 0.6686466 -0.4299604 69 2 spearmanpcor.test(FemaleSCbaseT1$SSSThril, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1143652 0.3567731 -0.9281316 69 2 spearmanpcor.test(FemaleSCbaseT1$SSSExp, FemaleSCbaseT1$SCbaseline, list(FemaleSCbaseT1$White, FemaleSCbaseT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1717418 0.1646317 -1.40551 69 2 spearmanCountdown
## HR Signaled
#SRP
pcor.test(FemaleHRCDT1$SRPTot, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.3687648 0.02688685 -2.313283 38 2 pearsonpcor.test(FemaleHRCDT1$SRPIPM, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.3019329 0.07350318 -1.846745 38 2 pearsonpcor.test(FemaleHRCDT1$SRPCA, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.2346641 0.1683209 -1.407621 38 2 pearsonpcor.test(FemaleHRCDT1$SRPELS, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.3266299 0.05185733 -2.015086 38 2 pearsonpcor.test(FemaleHRCDT1$SRPASB, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.3159753 0.06046844 -1.941927 38 2 spearman# ICU
pcor.test(FemaleHRCDT1$ICUTot, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.05017003 0.7713728 -0.2929079 38 2 spearmanpcor.test(FemaleHRCDT1$ICUCal, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.2124339 0.213545 -1.267625 38 2 spearmanpcor.test(FemaleHRCDT1$ICUUncare, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.08160535 0.6361114 -0.4774292 38 2 spearmanpcor.test(FemaleHRCDT1$ICUUnemo, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.05913598 0.731904 0.3454235 38 2 pearson# Lev
pcor.test(FemaleHRCDT1$LevTot, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.06647224 0.7001008 -0.3884556 38 2 pearsonpcor.test(FemaleHRCDT1$LevPrim, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.02536724 0.8832462 -0.1479628 38 2 spearmanpcor.test(FemaleHRCDT1$LevSec, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.06850159 0.6913887 -0.4003699 38 2 pearson# SSS
pcor.test(FemaleHRCDT1$SSSTot, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.3075942 0.06800677 -1.884954 38 2 pearsonpcor.test(FemaleHRCDT1$SSSDIS, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.3283233 0.0505838 -2.026792 38 2 spearmanpcor.test(FemaleHRCDT1$SSSBor, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1647106 0.3370734 -0.9737185 38 2 spearmanpcor.test(FemaleHRCDT1$SSSThril, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1405197 0.4136825 -0.8275747 38 2 spearmanpcor.test(FemaleHRCDT1$SSSExp, FemaleHRCDT1$CDHRCombAuciSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1657183 0.3340843 -0.9798439 38 2 spearman## HR Unsignaled
#SRP
pcor.test(FemaleHRCDT1$SRPTot, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.1105462 0.5209766 0.6485646 38 2 pearsonpcor.test(FemaleHRCDT1$SRPIPM, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.006528615 0.9698554 0.03806885 38 2 pearsonpcor.test(FemaleHRCDT1$SRPCA, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.0503173 0.7707197 0.2937699 38 2 pearsonpcor.test(FemaleHRCDT1$SRPELS, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.1399424 0.4156228 0.8241069 38 2 pearsonpcor.test(FemaleHRCDT1$SRPASB, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.02521714 0.8839323 0.1470867 38 2 spearman# ICU
pcor.test(FemaleHRCDT1$ICUTot, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1085369 0.5286261 -0.6366347 38 2 spearmanpcor.test(FemaleHRCDT1$ICUCal, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.0254988 0.8826448 -0.1487306 38 2 spearmanpcor.test(FemaleHRCDT1$ICUUncare, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.09435372 0.584127 -0.5526375 38 2 spearmanpcor.test(FemaleHRCDT1$ICUUnemo, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.2045449 0.2314394 -1.218453 38 2 pearson# Lev
pcor.test(FemaleHRCDT1$LevTot, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.0300487 0.8618902 -0.1752917 38 2 pearsonpcor.test(FemaleHRCDT1$LevPrim, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.0305373 0.8596666 0.1781446 38 2 spearmanpcor.test(FemaleHRCDT1$LevSec, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.00270151 0.987524 -0.01575243 38 2 pearson# SSS
pcor.test(FemaleHRCDT1$SSSTot, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.152125 0.3757747 0.8974792 38 2 pearsonpcor.test(FemaleHRCDT1$SSSDIS, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1599198 0.3515067 0.944642 38 2 spearmanpcor.test(FemaleHRCDT1$SSSBor, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.244036 0.1514779 1.467325 38 2 spearmanpcor.test(FemaleHRCDT1$SSSThril, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1389461 0.4189835 0.8181238 38 2 spearmanpcor.test(FemaleHRCDT1$SSSExp, FemaleHRCDT1$CDHRCombAuciUnSigaled, list(FemaleHRCDT1$White, FemaleHRCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.0635337 0.7127829 -0.3712119 38 2 spearman## SC signaled
#SRP
pcor.test(FemaleSCCDT1$SRPTot, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1163561 0.5056419 -0.6729863 37 2 pearsonpcor.test(FemaleSCCDT1$SRPIPM, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1283683 0.4623971 -0.7435718 37 2 pearsonpcor.test(FemaleSCCDT1$SRPCA, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.132148 0.4492095 -0.7658492 37 2 pearsonpcor.test(FemaleSCCDT1$SRPELS, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.05289124 0.762838 -0.3042629 37 2 pearsonpcor.test(FemaleSCCDT1$SRPASB, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.04988003 0.7759856 0.2868961 37 2 spearman# ICU
pcor.test(FemaleSCCDT1$ICUTot, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1204197 0.4907896 -0.6968292 37 2 spearmanpcor.test(FemaleSCCDT1$ICUCal, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1768983 0.3093521 1.032487 37 2 spearmanpcor.test(FemaleSCCDT1$ICUUncare, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.05323801 0.7613281 -0.3062634 37 2 spearmanpcor.test(FemaleSCCDT1$ICUUnemo, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1785845 0.3046884 -1.042651 37 2 pearson# Lev
pcor.test(FemaleSCCDT1$LevTot, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.06665225 0.7036315 -0.3837414 37 2 pearsonpcor.test(FemaleSCCDT1$LevPrim, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1946556 0.2624859 1.140018 37 2 spearmanpcor.test(FemaleSCCDT1$LevSec, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1636033 0.3476859 -0.9526654 37 2 pearson# SSS
pcor.test(FemaleSCCDT1$SSSTot, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.0402785 0.8183017 0.2315703 37 2 pearsonpcor.test(FemaleSCCDT1$SSSDIS, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.2368987 0.1706217 1.400753 37 2 spearmanpcor.test(FemaleSCCDT1$SSSBor, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.3218425 0.05937472 1.952744 37 2 spearmanpcor.test(FemaleSCCDT1$SSSThril, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.01009044 0.9541236 -0.05796811 37 2 spearmanpcor.test(FemaleSCCDT1$SSSExp, FemaleSCCDT1$CDSCCombAuciSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1480837 0.3959072 0.8601597 37 2 spearman## SC Unsignaled
#SRP
pcor.test(FemaleSCCDT1$SRPTot, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.01052575 0.9521467 -0.06046918 37 2 pearsonpcor.test(FemaleSCCDT1$SRPIPM, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.2059393 0.2352748 1.208945 37 2 pearsonpcor.test(FemaleSCCDT1$SRPCA, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.04704468 0.7884216 0.2705507 37 2 pearsonpcor.test(FemaleSCCDT1$SRPELS, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.1000495 0.5674288 -0.5776387 37 2 pearsonpcor.test(FemaleSCCDT1$SRPASB, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.28829 0.09305933 -1.72953 37 2 spearman# ICU
pcor.test(FemaleSCCDT1$ICUTot, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1571517 0.3672817 -0.9141261 37 2 spearmanpcor.test(FemaleSCCDT1$ICUCal, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.01075126 0.9511227 -0.06176483 37 2 spearmanpcor.test(FemaleSCCDT1$ICUUncare, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 -0.1084658 0.5351116 -0.6267866 37 2 spearmanpcor.test(FemaleSCCDT1$ICUUnemo, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.01871194 0.9150348 0.1075108 37 2 pearson# Lev
pcor.test(FemaleSCCDT1$LevTot, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.0705266 0.6872499 0.4061558 37 2 pearsonpcor.test(FemaleSCCDT1$LevPrim, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.006708969 0.9694886 0.03854096 37 2 spearmanpcor.test(FemaleSCCDT1$LevSec, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 -0.06266915 0.7206107 -0.3607159 37 2 pearson# SSS
pcor.test(FemaleSCCDT1$SSSTot, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "pearson") estimate p.value statistic n gp Method
1 0.0874113 0.6175618 0.5040691 37 2 pearsonpcor.test(FemaleSCCDT1$SSSDIS, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.2719087 0.1140728 1.623152 37 2 spearmanpcor.test(FemaleSCCDT1$SSSBor, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.3551021 0.03632389 2.18212 37 2 spearmanpcor.test(FemaleSCCDT1$SSSThril, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.1926206 0.2676052 1.127638 37 2 spearmanpcor.test(FemaleSCCDT1$SSSExp, FemaleSCCDT1$CDSCCombAuciUnSigaled, list(FemaleSCCDT1$White, FemaleSCCDT1$Age), method = "spearman") estimate p.value statistic n gp Method
1 0.03493665 0.8420732 0.2008184 37 2 spearman
Social Stressor